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3D reconstruction from a single 2D line drawing is an important but challenging problem in computer vision. Existed methods usually fail when line drawings contain large degree of noise named sketch errors. In this paper, we present an example-based approach to reconstructing 3D object, either planar or curved, from a single-view line drawing with sketch errors. Our method is to first decompose the input line drawing into primitive components and cluster them into local groups, then turn each group into 3D shapes via a novel example-based algorithm, and lastly integrate those recovered 3D shapes from all groups to build a final complete 3D model. Comprehensive experiments on a wide range of line drawings depicting man-made objects show that the proposed approach outperform previous work, especially for line drawings containing large degree of sketch errors. [ABSTRACT FROM AUTHOR]

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-á-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols. [ABSTRACT FROM AUTHOR]

Clustering plays an important role in discovering underlying patterns of data points according to their similarities. Many advanced algorithms have difficulty when dealing with variable clusters. In this paper, we propose a simple but effective clustering algorithm, CLUB. First, CLUB finds initial clusters based on mutual k nearest neighbours. Next, taking the initial clusters as input, it identifies the density backbones of clusters based on k nearest neighbours. Then, it yields final clusters by assigning each unlabelled point to the cluster which the unlabelled point's nearest higher-density-neighbour belongs to. To comprehensively demonstrate the performance of CLUB, we benchmark CLUB with six baselines including three classical and three state-of-the-art methods, on nine two-dimensional various-sized datasets containing clusters with various shapes and densities, as well as seven widely-used multi-dimensional datasets. In addition, we also use Olivetti Face dataset to illustrate the effectiveness of our method on face recognition. Experimental results indicate that CLUB outperforms the six compared algorithms in most cases. [ABSTRACT FROM AUTHOR]

In this paper we consider the shape space as the set of smooth simple closed curves in R 2 (parameterized curves), modulo translations, rotations and scale changes. An algorithm to obtain the intrinsic average of a sample data (set of planar shape realizations), from the identification of the shape space with an infinite dimensional Grassmannian is proposed using a gradient descent type algorithm. A simulation study is carried out to check the performance of the algorithm. [ABSTRACT FROM AUTHOR]

Density-based clustering algorithms are able to identify clusters of arbitrary shapes and sizes in a dataset which contains noise. It is well-known that most of these algorithms, which use a global density threshold, have difficulty identifying all clusters in a dataset having clusters of greatly varying densities. This paper identifies and analyses the condition under which density-based clustering algorithms fail in this scenario. It proposes a density-ratio based method to overcome this weakness, and reveals that it can be implemented in two approaches. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying densities that would otherwise impossible had the same algorithm been applied to the unscaled dataset. We provide an empirical evaluation using DBSCAN, OPTICS and SNN to show the effectiveness of these two approaches. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic niching clustering algorithm based on individual-connectedness (DNIC) is proposed for unsupervised classification with no prior knowledge. It aims to automatically evolve the optimal number of clusters as well as the cluster centers of the data set based on the proposed adaptive compact k -distance neighborhood algorithm. More specifically, with the adaptive selection of the number of the nearest neighbor and the individual-connectedness algorithm, DNIC often achieves several sets of connecting individuals and each set composes an independent niche. In practice, each set of connecting individuals corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. An application of the DNIC clustering algorithm in color image segmentation is also provided. Experimental results demonstrate that the DNIC clustering algorithm has high performance and flexibility. [ABSTRACT FROM AUTHOR]

Recently, some L1-norm-based principal component analysis algorithms with sparsity have been proposed for robust dimensionality reduction and processing multivariate data. The L1-norm regularization used in these methods encounters stability problems when there are various correlation structures among data. In order to overcome the drawback, in this paper, we propose a novel L1-norm-based principal component analysis with adaptive regularization (PCA-L1/AR) which can consider sparsity and correlation simultaneously. PCA-L1/AR is adaptive to the correlation structure of the training samples and can benefit both from L2-norm and L1-norm. An iterative procedure for solving PCA-L1/AR is also proposed. The experiment results on some data sets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel binary descriptor Boosted Local Binary (BLB) for object detection. The proposed descriptor encodes variable local neighbour regions in different scales and locations. Each region pair of the proposed descriptor is selected by the RealAdaBoost algorithm with a penalty term on the structural diversity. As a result, confident features that are good at describing specific characteristics will be chosen. Moreover, the encoding scheme is applied in the gradient domain in addition to the intensity domain, which is complementary to standard binary descriptors. The proposed method was tested using three benchmark object detection datasets, the CalTech pedestrian dataset, the FDDB face dataset, and the PASCAL VOC 2007 dataset. Experimental results demonstrate that the detection accuracy of the proposed BLB clearly outperforms traditional binary descriptors. It also achieves comparable performance with some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

We introduce a simple and effective concept for localizing objects in densely cluttered edge images based on shape information. The shape information is characterized by a binary template of the object's contour, provided to search for object instances in the image. We adopt a segment-based search strategy, in which the template is divided into a set of segments. In this work, we propose our own segment representation that we call one-pixel segment (OPS), in which each pixel in the template is treated as a separate segment. This is done to achieve high flexibility that is required to account for intra-class variations. OPS representation can also handle scale changes effectively. A dynamic programming algorithm uses the OPS representation to realize the search process, enabling a detailed localization of the object boundaries in the image. The concept's simplicity is reflected in the ease of implementation, as the paper's title suggests. The algorithm works directly with very noisy edge images extracted using the Canny edge detector, without the need for any preprocessing or learning steps. We present our experiments and show that our results outperform those of very powerful, state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Kernel Fisher discriminant analysis (KFDA) is a very popular learning method for the purpose of classification. In this paper, we propose a novel learning algorithm to improve KFDA and make it very suitable for dealing with the large-scale and high-dimensional data sets. The proposed algorithm is termed individualized KFDA (IKFDA). IKFDA is based on individualized learning , i.e., a strategy to learn and classify the individual test samples one by one. Our approach seeks to find the appropriate training subset, referred to as learning area , for each individual test sample, and then employ the learning area to construct the KFDA model for the test sample. For each individual test sample, IKFDA exploits some types of similarity measures to determine a learning area that consists of the training samples that are most similar to the test sample. Compared with the traditional learning algorithms that often exploit the whole training set to construct the learning models without considering the distribution property of the test samples, IKFDA can adaptively learn the individual test samples. It is a powerful tool to deal with the real-world complicated data sets that are often very large-scale and high-dimensional, and are usually drawn from the different distributions. Extensive experiments show that the proposed algorithm can obtain good classification results. [ABSTRACT FROM AUTHOR]

Density based clustering methods are proposed for clustering spatial databases with noise. Density Based Spatial Clustering of Applications with Noise (DBSCAN) can discover clusters of arbitrary shape and also handles outliers effectively. DBSCAN obtains clusters by finding the number of points within the specified distance from a given point. It involves computing distances from given point to all other points in the dataset. The conventional index based methods construct a hierarchical structure over the dataset to speed-up the neighbor search operations. The hierarchical index-structures fail to scale for datasets of dimensionality above 20. In this paper, we propose a novel graph-based index structure method Groups that accelerates the neighbor search operations and also scalable for high dimensional datasets. Experimental results show that the proposed method improves the speed of DBSCAN by a factor of about 1.5–2.2 on benchmark datasets. The performance of DBSCAN degrades considerably with noise due to unnecessary distance computations introduced by noise points while the proposed method is robust to noise by pruning out noise points early and eliminating the unnecessary distance computations. The cluster results produced by our method are exactly similar to that of DBSCAN but executed at a much faster pace. [ABSTRACT FROM AUTHOR]

When facing high dimensional data, dimension reduction is necessary before classification. Among dimension reduction methods, linear discriminant analysis (LDA) is a popular one that has been widely used. LDA aims to maximize the ratio of the between-class scatter and total data scatter in projected space, and the label of each data is necessary. However, in real applications, the labeled data are scarce and unlabeled data are in large quantity, so LDA is hard to be used under such case. In this paper, we propose a novel method named semi-supervised linear discriminant analysis (SLDA), which can use limited number of labeled data and a quantity of the unlabeled ones for training so that LDA can accommodate to the situation of a few labeled data available. Assuming that F represents the calculated class indicator matrix of the training data and Y denotes the true label of the labeled data, the objective function contains two parts: one is the criterion of LDA (which is a function of projection W , and a class indicator matrix F ), the other is the difference between the true data label and calculated label of these labeled data. As far as we know, there is no closed-form solution to the objective function. To solve such problem, we develop an iterative algorithm which calculates the class indicator matrix and the projection alternatively. The convergence of the proposed iterative algorithm is proved and confirmed by experiments. The experimental results on eight datasets show that the performance of SLDA is superior to that of traditional LDA and some state-of-the-art semi-supervised algorithms. [ABSTRACT FROM AUTHOR]

A competition in unsupervised color image segmentation took place in conjunction with the 22nd International Conference on Pattern Recognition (ICPR 2014). It aimed to promote evaluation of unsupervised color image segmentation algorithms using publicly available data sets, and to allow for any subsequent methods to be easily evaluated and compared with the results of the contested methods under identical conditions. Our comparison of different methods is based on the standard methodology of performance assessment using an on-line verification server. We present in this paper the evaluation of the top six results submitted to the ICPR 2014 contest in unsupervised color image segmentation and compare them with 11 other state-of-the-art unsupervised image segmenters. [ABSTRACT FROM AUTHOR]

An unsupervised learning algorithm with density information considered is proposed for congested scene classification. Though many works have been proposed to address general scene classification during the past years, congested scene classification is not adequately studied yet. In this paper, an efficient unsupervised feature learning approach with density information encoded is proposed to solve this problem. Based on spherical k -means, a feature selection process is proposed to eliminate the learned noisy features. Then, local density information which better reflects the crowdedness of a scene is encoded by a novel feature pooling strategy. The proposed method is evaluated on the assembled congested scene data set and UIUC-sports data set, and intensive comparative experiments justify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we present the Delta Medial Axis (DMA), a quasi-linear algorithmic solution addressing several of the main concerns of discrete medial axes (MA) computation. First, its sensitivity to small shape perturbations is counterbalanced by a single parameter (delta), used in a pruning strategy that implicitly takes into account the local topology of the boundary. Second, the discrete nature of images is addressed by approximating the original MA definition in continuous space. Third, to allow real-time performances, a set of algorithmic optimizations is proposed. We compare our approach both qualitatively and quantitatively to recent state of the art solutions, and show that the DMA presents an excellent choice for a wide range of applications. Finally, to allow fast and efficient use of our algorithm, we propose the complete pseudo-code. [ABSTRACT FROM AUTHOR]

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the conventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recognition tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• A hybrid Machine Learning (ML) approach for an efficient wrapper feature selection is proposed for enhancing the performance and reducing the computation time of IDS while increasing the accuracy to detect the intruder in IoT networks. • The hybrid ML method consists of two techniques: Differential Evolution (DE) and Extreme Learning Machine (ELM). The DE is responsible for choosing the beneficial features while the ELM is employed to assess the selected features. • Extensive experiments are implemented to evaluate the proposed approach using different performance metrics. In addition, to verify the efficiency of proposed model, the comparison is achieved with some related existing methods such as CFS + SVM [20] , CFS + ANN [19] , GA + Bagging [18] , CFS + RF [30] , and Chi-squared + RF [30]. The intrusion detection system (IDS) has gained a rapid increase of interest due to its widely recognized potential in various security fields, however, it suffers from several challenges. Different network datasets have several redundant and irrelevant features that affect the decision of the IDS classifier. Therefore, it is essential to decrease these features to improve the system performance. In this paper, an efficient wrapper feature selection method is proposed for improving the performance and decreasing the processing time of the IDS. The proposed approach employs a differential evaluation algorithm to select the useful features whilst the extreme learning machine classifier is applied after feature selection to evaluate the selected features. Many experiments are performed using the full NSL-KDD dataset to evaluate the performance of the proposed method. The results prove that the proposed approach can efficiently reduce the features, increase the accuracy, reduce the false alarm rates, and improve the processing time of the IDS in comparison to other recent related works. [ABSTRACT FROM AUTHOR]

• We proposed a novel AdaptiveWeighted Guided Image Filtering algorithm which outperforms the existing guided filters to preserve accurate edges. • We designed four special cases of the adaptive adjustment algorithm to remove the noise while enhancing depth details. • We proposed a new strategy based on AWGIF algorithm for depth enhancement in Shape-From-Focus. • Our proposed method are applied to the emerging AR/VR on hand-held devices in real-time. Existing shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. Moreover, noise in the sequence affects the accuracy of the depth map. In this paper, a novel depth enhancement algorithm for the SFF based on an adaptive weighted guided image filtering (AWGIF) is proposed to address the above issues. The AWGIF is applied to decompose an initial depth map estimated by the traditional SFF into base and detail layers. In order to preserve the edges accurately in the refined depth map, the guidance image is constructed from the sequence, and the coefficient of the AWGIF is utilized to suppress the noise while enhancing the fine depth details. Experiments on real and synthetic objects demonstrate the superiority of our algorithm in terms of anti-noise, and the ability to preserve depth edges and fine structural details w.r.t. existing methods. [ABSTRACT FROM AUTHOR]

• This paper develops a novel framework for a family of correlation classifiers. • A family of correlation classifiers are designed from the pessimistic viewpoint under possible perturbation of data. • The proximal majorization-minimization optimization (PMMO) is used to solve the proposed model. • The convergence rate of the algorithm in terms of different criteria is given. • The experiments have been conducted to demonstrate the effectiveness of the proposed approach. This paper develops a novel framework for a family of correlation classifiers that are reconstructed from uncertain convex programs under data perturbation. Under this framework, correlation classifiers are exploited from the pessimistic viewpoint under possible perturbation of data, and the max-min optimization problem is formulated by simplifying the original model in terms of adaptive uncertainty regions. The proposed model can be formulated as a minimization problem under proper conditions. The proximal majorization-minimization optimization (PMMO) based on Bregman divergences is devised to solve the proposed model that may be nonconvex or nonsmooth. It is found that using PMMO to solve the proposed model can exploit the convergence rate of the solution sequence in the nonconvex case. In the case of specific functions we can use the accelerated versions of first-order methods to solve the proposed model with convexity in order to make them have fast convergence rates in terms of the objective function. Extensive experiments on some data sets are conducted to demonstrate the feasibility and validity of the proposed model. [ABSTRACT FROM AUTHOR]

Kernel possibilistic fuzzy C-means with local information (KWPFLICM) has important research significance of image segmentation, but it is very sensitive to high noise or outliers. To enhance the segmentation performance of the algorithm, this paper proposes a kernelized total Bregman divergence-driven possibilistic fuzzy clustering with local information (TKWPFLICM). Firstly, a polynomial kernel function is introduced to kernelize total Bregman divergence (TBD), and local neighborhood information of the pixel is used to modify it, which overcomes the shortcomings of Bregman divergence (BD) with rotation variability; Secondly, the modified kernelized TBD and possibilistic typicality are combined to further enhance the anti-noise ability of the algorithm; Finally, the modified kernelized TBD is introduced into the objective function of KWPFLICM algorithm, then a novel robust fuzzy clustering algorithm is derived by optimization theory. Experimental results show that compared with existing fuzzy clustering-related algorithms, the average SA improvement on TKWPFLICM algorithm is in the range of 0.791% to 33.237%. Therefore, TKWPFLICM algorithm has better anti-noise robustness and segmentation accuracy. [ABSTRACT FROM AUTHOR]

Circle detection using randomized sampling has been developed in recent years to reduce computational intensity. However, randomized sampling is sensitive to noise that can lead to reduced accuracy and false-positive candidates. To improve on the robustness of randomized circle detection under noisy conditions this paper presents a new methodology for circle detection based upon randomized isosceles triangles sampling. It is shown that the geometrical property of isosceles triangles provides a robust criterion to find relevant edge pixels which, in turn, offers an efficient means to estimate the centers and radii of circles. For best efficiency, the estimated results given by the sampling from individual connected components of the edge map were analyzed using a simple clustering approach. To further improve on the accuracy we applied a two-step refinement process using chords and linear error compensation with gradient information of the edge pixels. Extensive experiments using both synthetic and real images have been performed. The results are compared to leading state-of-the-art algorithms and it is shown that the proposed methodology has a number of advantages: it is efficient in finding circles with a low number of iterations, it has high rejection rate of false-positive circle candidates, and it has high robustness against noise. All this makes it adaptive and useful in many vision applications. [ABSTRACT FROM AUTHOR]

Though most of the faces are axis-symmetrical objects, few real-world face images are axis-symmetrical images. In the past years, there are many studies on face recognition, but only little attention is paid to this issue and few studies to explore and exploit the axis-symmetrical property of faces for face recognition are conducted. In this paper, we take the axis-symmetrical nature of faces into consideration and design a framework to produce approximately axis-symmetrical virtual dictionary for enhancing the accuracy of face recognition. It is noteworthy that the novel algorithm to produce axis-symmetrically virtual face images is mathematically very tractable and quite easy to implement. Extensive experimental results demonstrate the superiority in face recognition of the virtual face images obtained using our method to the original face images. Moreover, experimental results on different databases also show that the proposed method can achieve satisfactory classification accuracy in comparison with state-of-the-art image preprocessing algorithms. The MATLAB code of the proposed method can be available at http://www.yongxu.org/lunwen.html . [ABSTRACT FROM AUTHOR]

Fingerprint indexing is critical for identifying fingerprints efficiently in large-scaled fingerprint databases. The state-of-the-art indexing accuracies are achieved by minutiae-based indexing approaches. A major difference of these approaches is how they deal with fingerprint registration problem. Some of the approaches do not use registration, while others perform relative/pairwise registration (e.g., based on mated minutiae between two fingerprints). However, the former approach is not accurate, since without geometric constraints, many false matches can be found even for non-mated fingerprints. The latter is not efficient for large databases since the relative registration step has to be performed for each pair of fingerprints. It is desirable to develop an absolute registration approach, which can register any single fingerprint into a common coordinate system, and therefore can address efficiency and accuracy problems simultaneously. In this paper, we proposed a fingerprint pose estimation algorithm which can register fingerprints into a common finger coordinate system. Fingerprint pose estimation problem is viewed as a two-class classification problem and approached by sliding window classifiers trained on labeled data. Ridge orientation information is used as features instead of singularities which are often affected by noise. With the pose estimation, we are able to refine the matched minutiae with global spatial constraint. By combining the proposed pose estimation algorithm with an improved Locality Sensitive Hashing algorithm for MCC descriptor, our indexing system outperformed previous state-of-the-art on widely used databases and its scalability was tested on a large database with one million fingerprints. [ABSTRACT FROM AUTHOR]

Recent advances in image classification mostly rely on the use of powerful local features combined with an adapted image representation. Although Convolutional Neural Network (CNN) features learned from ImageNet were shown to be generic and very efficient, they still lack of flexibility to take into account variations in the spatial layout of visual elements. In this paper, we investigate the use of structural representations on top of pretrained CNN features to improve image classification. Images are represented as strings of CNN features. Similarities between such representations are computed using two new edit distance variants adapted to the image classification domain. Our algorithms have been implemented and tested on several challenging datasets, 15Scenes, Caltech101, Pascal VOC 2007 and MIT indoor. The results show that our idea of using structural string representations and distances clearly improves the classification performance over standard approaches based on CNN and SVM with linear kernel, as well as other recognized methods of the literature. [ABSTRACT FROM AUTHOR]

This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using their difference of convex representations. A non-smooth non-convex optimization formulation of the clustering problem is used to design the algorithm. Characterizations of critical points, stationary points in the sense of generalized gradients and inf-stationary points of the clustering problem are given. The proposed algorithm is tested and compared with other clustering algorithms using large real world data sets. [ABSTRACT FROM AUTHOR]

This paper proposes a homograhpy estimation algorithm that uses contour model to track and locate the texture-less object in high clutter environments. The proposed method consists of a homography recognition with the correspondences between model and image lines unknown, followed by a nonlinear homography optimization based on the maximum likelihood criterion. In our approach, the initial estimation of the homography is obtained in the framework of RANSAC-like, which contains two stages: hypothesizing and verifying. The first stage generates a number of homograhpy hypotheses from the correspondences of the quadrangle-like structures. Next, the homography hypotheses are quickly ranked according to a redefined distance function. In the refinement procedure, the model sample points are projected into the image plane. After that, 1D search is utilized along the normal direction to obtain the corresponding image point for each model sample point. Finally, the optimized homography is obtained by minimizing the errors between the sample points and their corresponding image points. Experiments show that the proposed method performs robust homography recognition of texture-less planar objects and maintains accurate and stable homography estimation in the cluttered environments as well as the cases of extreme slant angles. [ABSTRACT FROM AUTHOR]

This paper presents kernel-based hard clustering methods with kernelization of the metric and automatic weighting of the variables. The proposed methodology is supported by the fact that a kernel function can be written as a sum of kernels evaluated separately on each variable. Thus, in the proposed algorithms dissimilarity measures are obtained as sums of Euclidean distances between patterns and centroids computed individually for each variable by means of kernels. The main advantage of this approach over the conventional approach is that it allows the use of kernelized adaptive distances, which are suitable to learn the weights of the variables dynamically, improving the performance of the algorithms. Moreover, various partition and cluster interpretation tools are introduced. Experiments with a number of benchmark datasets corroborate the usefulness of the proposed algorithms and the merit of the partition and cluster interpretation tools. [ABSTRACT FROM AUTHOR]

This paper proposes a video super-resolution method based on an adaptive superpixel-guided auto-regressive (AR) model. Key-frames are automatically selected and super-resolved by a sparse regression method. Non-key-frames are super-resolved by exploiting the spatio-temporal correlations: the temporal correlation is exploited by an optical flow method while the spatial correlation is modeled by a superpixel-guided AR model. Experimental results show that the proposed method outperforms state-of-the-art methods in terms of both subjective visual quality and objective peak signal-to-noise ratio (PSNR). The proposed method requires less computation and is suitable for practical applications. [ABSTRACT FROM AUTHOR]

SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-naive Bayesian classifiers which relax the attribute independence assumption of Naive Bayes (NB) to allow each attribute to depend on a common single attribute (superparent). SPODEs can effectively handle data with attribute dependency but still inherent NB׳s key advantages such as computational efficiency and robustness for high dimensional data. In reality, determining an optimal superparent for SPODEs is difficult. One common approach is to use weighted combinations of multiple SPODEs, each having a different superparent with a properly assigned weight value ( i.e ., a weight value is assigned to each attribute). In this paper, we propose a self-adaptive SPODEs, namely SODE, which uses immunity theory in artificial immune systems to automatically and self-adaptively select the weight for each single SPODE. SODE does not need to know the importance of individual SPODE nor the relevance among SPODEs, and can flexibly and efficiently search optimal weight values for each SPODE during the learning process. Extensive experiments and comparisons on 56 benchmark data sets, and validations on image and text classification, demonstrate that SODE outperforms state-of-the-art weighted SPODE algorithms and is suitable for a wide range of learning tasks. Results also confirm that SODE provides an appropriate balance between runtime efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Recently, saliency detection has been becoming a popular topic in computer vision. In this paper we propose an object-level saliency detection algorithm which explicitly explores bottom-up visual attention and objectness cues. Firstly, some category-independent object candidates are segmented from the image by the quantized color attributes. Then two metrics, global cues and candidate objectness, are developed to estimate the saliency in the whole image and the object candidates respectively. We use global cues to describe the focusness and spatial distribution of color attributes in the entire image. As supplementary, candidate objectness can reveal the objectness of the object candidates. Finally, we integrate the two saliency cues to derive a saliency map of the image. By explicitly fusing candidate objectness and global cues, our proposed method is more suitable for processing images with complex background. Experimental results on three large benchmark databases demonstrate that the proposed method achieves more favorable performance than 15 recent state-of-the-art methods in terms of three evaluation criterions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm for power line identification and extraction from high resolution remote sensing images. Theoretically, it is difficult to detect power lines in satellite images due to some characteristics, such as sub-pixel, weak target, discrete and the complicated background. To our knowledge, the problem of extraction of the power lines from satellite images is faced for the first time. An improved Radon transform, Cluster Radon Transform (CRT), was developed to extract linear feature from satellite image. Compared with conventional Radon transform, CRT can efficiently avoid false alarm. After that, a set of rules of power lines was abstracted to distinguish power lines from other linear feature, such as roads. The experimental results show that CRT not only has strong anti-noise capability to random noise, but also has strong anti-noise capability to system noise caused by non- linear feature. Furthermore, CRT also has the strong capability to detect short segment in an image. Finally, synthetic images and true images were used to verify the new approach. The achievement has potential to be applicable not only to the power line extraction, but also to other weak linear target detection. [ABSTRACT FROM AUTHOR]

This paper presents a high-level hand feature extraction method for real-time gesture recognition. Firstly, the fingers are modelled as cylindrical objects due to their parallel edge feature. Then a novel algorithm is proposed to directly extract fingers from salient hand edges. Considering the hand geometrical characteristics, the hand posture is segmented and described based on the finger positions, palm center location and wrist position. A weighted radial projection algorithm with the origin at the wrist position is applied to localize each finger. The developed system can not only extract extensional fingers but also flexional fingers with high accuracy. Furthermore, hand rotation and finger angle variation have no effect on the algorithm performance. The orientation of the gesture can be calculated without the aid of arm direction and it would not be disturbed by the bare arm area. Experiments have been performed to demonstrate that the proposed method can directly extract high-level hand feature and estimate hand poses in real-time. [ABSTRACT FROM AUTHOR]

In this paper, we present an association based tracking approach to track multiple insect body parts in a set of low frame-rate videos. The association is formulated as a MAP problem and solved by the Hungarian algorithm. Different from a traditional track-and-then-rectification scheme, this framework refines the tracking hypotheses in an interactive fashion: it integrates a key frame selection approach to minimize the number of frames for user correction while optimizing the final hypotheses. Given user correction, it takes user inputs to rectify the incorrect hypotheses on the other frames. Thus, the framework improves the tracking accuracy by introducing active key frame selection and interactive components, enabling a flexible strategy to achieve a trade-off between human effort and tracking precision. Given the refined tracks at a bounding box (BB) level, the tip of each body part is estimated, and multiple body parts in a BB are further differentiated. The efficiency and the effectiveness of the framework are verified on challenging video datasets for insect behavioral experiments. [ABSTRACT FROM AUTHOR]

High dimensional data with thousands of features present a big challenge to current clustering algorithms. Sparsity, noise and correlation of features are common characteristics of such data. Another common phenomenon is that clusters in such high dimensional data often exist in different subspaces. Ensemble clustering is emerging as a prominent technique for improving robustness, stability and accuracy of high dimensional data clustering. In this paper, we propose a stratified sampling method for generating subspace component data sets in ensemble clustering of high dimensional data. Instead of randomly sampling a subset of features for each component data set, in this method we first cluster the features of high dimensional data into a few feature groups called feature strata. Using stratified sampling, we randomly sample some features from each feature stratum and merge the sampled features from different feature strata to generate a component data set. In this way, the component data sets have better representations of the clustering structure in the original data set. Comparing with random sampling and random projection methods in synthetic data analysis, the component clustering by stratified sampling has demonstrated that the average clustering accuracy was increased without sacrificing clustering diversity. We carried out a series of experiments on eight real world data sets from microarray, text and image domains to evaluate ensemble clustering methods using three subspace component data generation methods and four consensus functions. The experimental results consistently showed that the stratified sampling method produced the best ensemble clustering results in all data sets. The ensemble clustering with stratified sampling also outperformed three other ensemble clustering methods which generate component clusters from the entire space of the original data. [ABSTRACT FROM AUTHOR]

Classification is an essential task for predicting the class values of new instances. Both k -fold and leave-one-out cross validation are very popular for evaluating the performance of classification algorithms. Many data mining literatures introduce the operations for these two kinds of cross validation and the statistical methods that can be used to analyze the resulting accuracies of algorithms, while those contents are generally not all consistent. Analysts can therefore be confused in performing a cross validation procedure. In this paper, the independence assumptions in cross validation are introduced, and the circumstances that satisfy the assumptions are also addressed. The independence assumptions are then used to derive the sampling distributions of the point estimators for k -fold and leave-one-out cross validation. The cross validation procedure to have such sampling distributions is discussed to provide new insights in evaluating the performance of classification algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a framework to fit data to a model consisting of multiple connected ellipses. For each iteration of the fitting algorithm, the representation of the multiple ellipses is mapped to a Gaussian mixture model (GMM) and the connections are mapped to geometric constraints for the GMM. The fitting is a modified constrained expectation maximisation (EM) method on the GMM (maximising with respect to the ellipse parameters rather than Gaussian parameters). A key modification is that the precision of the chosen GMM is increased at each iteration. This is similar to slowly inflating a bunch of connected balloons and so this is called balloon fitting. Extensions of the framework to other constraints and possible pre-processing are also discussed. The superiority of balloon fitting is demonstrated through experiments on several silhouettes with noisy edges which compare other existing methods with balloon fitting and some of the extensions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel cross-trees structure to perform the non-local cost aggregation strategy, and the cross-trees structure consists of a horizontal-tree and a vertical-tree . Compared to other spanning trees, the significant superiorities of the cross-trees are that the trees׳ constructions are efficient and the trees are exactly unique since the constructions are independent on any local or global property of the image itself. Additionally, two different priors: edge prior and superpixel prior , are proposed to tackle the false cost aggregations which cross the depth boundaries. Hence, our method contains two different algorithms in terms of cross-trees+prior . By traversing the two crossed trees successively, a fast non-local cost aggregation algorithm is performed twice to compute the aggregated cost volume. Performance evaluation on the 27 Middlebury data sets shows that both our algorithms outperform the other two tree-based non-local methods, namely minimum spanning tree (MST) and segment-tree (ST). [ABSTRACT FROM AUTHOR]

Twin K-class support vector classification (Twin-KSVC) is a novel multi-class method based on twin support vector machine (TWSVM). In this paper, we formulate a least squares version of Twin-KSVC called as LST-KSVC. This formulation leads to extremely simple and fast algorithm. LST-KSVC, same as the Twin-KSVC, evaluates all the training data into a “1-versus-1-versus-rest” structure, so it generates ternary output {−1, 0, +1}. In LST-KSVC, the solution of the two modified primal problems is reduced to solving only two systems of linear equations whereas Twin-KSVC needs to solve two quadratic programming problems (QPPs) along with two systems of linear equations. Our experiments on UCI and face datasets indicate that the proposed method has comparable accuracy in classification to that of Twin-KSVC but with remarkably less computational time. Also, because of the structure “1-versus-1-versus-rest”, the classification accuracy of LST-KSVC is higher than typical multi-class method based on SVMs. [ABSTRACT FROM AUTHOR]

Dataset shift is a very common issue wherein the input data distribution shifts over time in non-stationary environments. A broad range of real-world systems face the challenge of dataset shift. In such systems, continuous monitoring of the process behavior and tracking the state of shift are required in order to decide about initiating adaptive corrections in a timely manner. This paper presents novel methods for covariate shift-detection tests based on a two-stage structure for both univariate and multivariate time-series. The first stage works in an online mode and it uses an exponentially weighted moving average (EWMA) model based control chart to detect the covariate shift-point in non-stationary time-series. The second stage validates the shift-detected by first stage using the Kolmogorov–Smirnov statistical hypothesis test (K–S test) in the case of univariate time-series and the Hotelling T -Squared multivariate statistical hypothesis test in the case of multivariate time-series. Additionally, several orthogonal transformations and blind source separation algorithms are investigated to counteract the adverse effect of cross-correlation in multivariate time-series on shift-detection performance. The proposed methods are suitable to be run in real-time. Their performance is evaluated through experiments using several synthetic and real-world datasets. Results show that all the covariate shifts are detected with much reduced false-alarms compared to other methods. [ABSTRACT FROM AUTHOR]

Establishing correct correspondences between two faces with different viewpoints has played an important role in 3D face reconstruction and other computer-vision applications. Usually, face images are considered to lack sufficient distinctive features to establish a large number of correspondences on uncalibrated images. In this paper, we investigate pore-scale facial features, which are formed from pores, fine wrinkles, and hair. These features have many characteristics that make them suitable for matching facial images under different variations. Using both biological observation and computer-vision consideration, a new framework is devised for pore-scale facial-feature extraction and matching. The matching difficulty under various skin appearances of different subjects and imaging distortion is also analyzed. For further improving the matching performance and tackling distortions such as varying illuminations and unfocused blurring, a pore-to-pore correspondences dataset is established for training a more distinctive and compact descriptor. Experiments are conducted on a face database containing 105 subjects, and the results prove that the pore-scale features are highly distinctive; face images with a minimum resolution of 600×700 (0.4 mega) pixels contain sufficient details to perform a reliable matching in different poses. Generally, our algorithm can establish between 500 and 2000 correct correspondences on a pair of uncalibrated face images of the same person. Furthermore, the proposed methods can be applied to face recognition, 3D reconstruction, etc. [ABSTRACT FROM AUTHOR]

Facial wrinkles are important features of aging human skin which can be incorporated in several image-based applications related to aging. Facial wrinkles are 3D features of skin and appear as subtle discontinuities or cracks in surrounding skin texture. However, facial wrinkles can easily be masked by illumination/acquisition conditions in 2D images due to the specific nature of skin surface texture and its reflective properties. Existing approaches to image-based analysis of aging skin are based on the analysis of wrinkles as texture and not as curvilinear discontinuity/crack features. Previously, we proposed a stochastic approach based on Marked Point Processes (MPP) to localize facial wrinkles as curves. In this paper, we present a fast deterministic algorithm based on Gabor filters and image morphology to improve localization results. We propose image features based on Gabor filter bank to highlight the subtle curvilinear discontinuities in skin texture caused by wrinkles. Then, image morphology is used to incorporate geometric constraints to localize curvilinear shapes of wrinkles at image sites of large Gabor filter responses. Experiments are conducted on two sets of low and high resolution images and results are compared with those of MPP modeling. Experiments show that the proposed algorithm not only is significantly faster than MPP-based approach but also provides visually better results. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for the classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease. [ABSTRACT FROM AUTHOR]

In this paper, we develop a computer aided diagnosis algorithm to detect and classify the abnormalities in vision-based endoscopic examination. We focus on analyzing the traditional gastroscope data and help the medical experts improve the accuracy of medical diagnosis with our analysis tool. To achieve this, we first segment the image into superpixels, then extract various color and texture features from them and combine the features into one feature vector to represent the images. This approach is more flexible and accurate than the traditional patch-based image representation. Then we design a novel feature selection model with group sparsity, Deep Sparse SVM (DSSVM) that not only can assign a suitable weight to the feature dimensions like the other traditional feature selection models, but also directly exclude useless features from the feature pool. Thus, our DSSVM model can maintain the accuracy while reducing the computation complexity. Moreover, the image quality is also pre-assessed. For the experiments, we build a new gastroscope dataset with a total of about 3800 images from 1284 volunteers, and conducted various experiments and comparisons with other algorithms to justify the effectiveness and efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

• We propose incomplete multi-view clustering with cosine similarity (IMCCS) for partitioning incomplete multi-view data. • IMCCS calculates the cosine similarity of incomplete multi-view data in the original multi-view space. • Gradient descent with the multiplicative update rule is presented to solve the objective of IMCCS. • IMCCS outperforms state-of-the-art incomplete multi-view clustering methods. Incomplete multi-view clustering partitions multi-view data suffering from missing views, for which matrix factorization approaches seek the latent representation of incomplete multi-view data and constitute one effective category of methods. To exploit data properties further, manifold structure preserving is also incorporated into matrix factorization. However, previous methods optimized the data similarity matrix in the manifold structure preserving term as an unknown variable, which is not guaranteed to faithfully represent the similarities of the original multi-view data and also increases the computational difficulty. To overcome these drawbacks, in this paper, we propose Incomplete Multi-view Clustering with Cosine Similarity (IMCCS). In IMCCS, we directly calculate the cosine similarity in the original multi-view space to strengthen the ability of preserving the manifold structure of the original multi-view data. There is no need to introduce the additional variable. The manifold structure preserving term with cosine similarity and the matrix factorization term are integrated into a unified objective function. An iterative algorithm with gradient descent is designed to solve this objective. Extensive experiments on multi-view datasets show that IMCCS outperforms state-of-the-art incomplete multi-view clustering methods. [ABSTRACT FROM AUTHOR]

• We propose a systematic approach to find bases of moment invariants with respect to orthogonal transformations using the generator method for scalar, vector, and tensor fields in two and three dimensions. • We show that it is always possible to construct a basis using all homo- 5 geneous invariants and simultaneous invariants with no more than two different moment tensors. • To the best of our knowledge, this results in the first 3D generator ap- proach that produces bases that are complete, independent, and exible, i.e., working for any input pattern. • We reveal so far unknown structural similarity between the 3D generator approach and its 2D counterpart as well as between the 3D generator approach and the 3D normalization approach. Moment invariants have been successfully applied to pattern detection tasks in 2D and 3D scalar, vector, and matrix valued data. However so far no flexible basis of invariants exists, i.e., no set that is optimal in the sense that it is complete and independent for every input pattern. In this paper, we prove that a basis of moment invariants can be generated that consists of tensor contractions of not more than two different moment tensors each under the conjecture of the set of all possible tensor contractions to be complete. This result allows us to derive the first generator algorithm that produces flexible bases of moment invariants with respect to orthogonal transformations by selecting a single non-zero moment to pair with all others in these two-factor products. Since at least one non-zero moment can be found in every non-zero pattern, this approach always generates a complete set of descriptors. [ABSTRACT FROM AUTHOR]

• Efficient kNN search based on feature learning, clustering, and adaptive k values. • Several proposals to automatically optimize the search parameters. • Comprehensive experimentation with 10 datasets of different typology and size. • Results demonstrate that the proposal outperforms state-of-the-art methods. The k -Nearest Neighbor (k NN) algorithm is widely used in the supervised learning field and, particularly, in search and classification tasks, owing to its simplicity, competitive performance, and good statistical properties. However, its inherent inefficiency prevents its use in most modern applications due to the vast amount of data that the current technological evolution generates, being thus the optimization of k NN-based search strategies of particular interest. This paper introduces the caKD+ algorithm, which tackles this limitation by combining the use of feature learning techniques, clustering methods, adaptive search parameters per cluster, and the use of pre-calculated K-Dimensional Tree structures, and results in a highly efficient search method. This proposal has been evaluated using 10 datasets and the results show that caKD+ significantly outperforms 16 state-of-the-art efficient search methods while still depicting such an accurate performance as the one by the exhaustive k NN search. [ABSTRACT FROM AUTHOR]

This paper introduces a method for achieving online Bayesian learning: • for time series data of indefinite (possibly infinite) length, • where the model is statistically non-stationary over time, and • the model includes a circular variable. The integrals required for full Bayesian inference are intractable, so we use a varational approximation method and place priors over precision hyperparameters to ensure that • the posterior probability distributions do not become overly tight, which would impede its ability to recognise and track changes, and • no values in the system are able to continuously increase and hence exceed the numerical representation of the programming language. With only a single round of updates in the variational Bayesian scheme per observation, a processing rate of at least 11 kHz is achieved. We introduce an online variational Bayesian model for tracking changes in a non-stationary, multivariate, temporal signal, using as an example the changing frequency and amplitude of a noisy sinusoidal signal over time. The model incorporates each observation as it arrives and then discards it, and places priors over precision hyperparameters to ensure that (i) the posterior probability distributions do not become overly tight, which would impede its ability to recognise and track changes, and (ii) no values in the system are able to continuously increase and hence exceed the numerical representation of the programming language. It is thus able to perform truly online processing for an infinitely long set of observations. Only a single round of updates in the variational Bayesian scheme per observation is used, and the complexity of the algorithm is constant in time. The proposed method is demonstrated on a large number of synthetic datasets, comparing the results from the full model (with precision hyperparameters as variables with priors) with those from the base model where the precision hyperparameters are fixed values. The full model is also demonstrated on a set of real climate data. [ABSTRACT FROM AUTHOR]

• A novel non-parametric clustering algorithm based on the concept of divide-and-merge. • Ability to discover both convex and non-convex shaped clusters. • Ability to discover clusters different in densities. • Ability to detect and remove outliers/noise in the data. • Usability for high dimensional data and easily tunable or fixed hyperparameters. In this paper, a novel non-parametric clustering algorithm which is based on the concept of divide-and-merge is proposed. The proposed algorithm is based on two primary phases, after data cleaning: (i) the Division phase and (ii) the Merging phase. In the initial phase of division, the data is divided into an optimized number of small sub-clusters utilizing all the dimensions of the data. In the second phase of merging, the small sub-clusters obtained as a result of division are merged according to an advanced statistical metric to form the actual clusters in the data. The proposed algorithm has the following merits: (i) ability to discover both convex and non-convex shaped clusters, (ii) ability to discover clusters different in densities, (iii) ability to detect and remove outliers/noise in the data (iv) easily tunable or fixed hyperparameters (v) and its usability for high dimensional data. The proposed algorithm is extensively tested on 20 benchmark datasets including both, the synthetic and the real datasets and is found better/competing to the existing state-of-the-art parametric and non-parametric clustering algorithms. [ABSTRACT FROM AUTHOR]

• A novel unsupervised feature selection method is proposed which aims at preserving the local invariance of feature subspace by local linear embedding and manifold regularization simultaneously. • An alternating algorithm based on alternative direction method of multiplier s with computational complexity analysis is designed to handle the resulting optimization problem. • A variety of experiments on several publicly available real world datasets are conducted to demonstrate the effectiveness and superiority of the proposed method. As one of the important dimensionality reduction techniques, unsupervised feature selection (UFS) has enjoyed amounts of popularity over the last few decades, which can not only improve learning performance, but also enhance interpretability and reduce computational costs. The existing UFS methods often model the data in the original feature space, which cannot fully exploit the discriminative information. In this paper, to address this issue, we investigate how to strengthen the relationship between UFS and the feature subspace, so as to select relevant features more straightforwardly and effectively. Methodologically, a novel UFS approach, referred to as Graph Regularized Local Linear Embedding (GLLE), is proposed by integrating local linear embedding (LLE) and manifold regularization constrained in feature subspace into a unified framework. To be more specific, we explicitly define a feature selection matrix composed of 0 and 1, which can realize the process of UFS. For the purpose of modelling the feature selection matrix, we propose to preserve the local linear reconstruction relationship among neighboring data points in the feature subspace, which corresponds to LLE constrained in the feature subspace. To make the feature selection matrix more accurate, we propose to use manifold regularization as an assistant of LLE to find the relevant and representative features such that the selected features can make each sample under the feature subspace be accordance with the manifold assumption. A tailored iterative algorithm based on Alternative Direction Method of Multipliers (ADMM) is designed to solve the proposed optimization problem. Extensive experiments on twelve real-world benchmark datasets are conducted, and the more promising results are achieved compared with the state-of-the-arts approaches. [ABSTRACT FROM AUTHOR]

• An innovatively improved SIFT structure that addresses the thorough feature extraction in all forgery cases. • A fast keypoint-label matching (FKLM) that creates groups of keypoint-label so that every high-dimensional feature is assigned into one of these groups. • A coarse-to-fine filtering relying on intrinsic attributes of exact keypoint-matches is designed to reduce false keypoint-matches effectively. • The adaptive block filling relying on true keypoint-matches contributes to the accurate and efficient suspicious region filling. Video copy-move forgery detection (VCMFD) is a significant and greatly challenging task due to a variety of difficulties, including a huge amount of video information, diverse forgery types, rich forgery objects, and homogenous forgery sources. These difficulties raise four unresolved key challenges in VCMFD: i) ineffective detection in some popular forgery cases; ii) inefficient matching in processing numerous video pixels with hundred-dimensional features under dozens of matching iterations; iii) high false positive (F P) in detecting forgery videos; iv) low trade-off of efficiency and effectiveness in filling forgery region, and even failing in indicating forgeries at the pixel level. In this paper, a novel VCMFD method is proposed to address these issues: i) an innovatively improved SIFT structure that can address the thorough feature extraction in all video copy-move forgery cases; ii) a novel fast keypoint-label matching (FKLM) algorithm is proposed that creates some keypoint-label groups so that every high-dimensional feature is assigned into one of these groups. As a result, matching of video pixels can be directly done on a small number of keypoint-label groups only, leading to a nearly 500% raise in matching efficiency; iii) a new coarse-to-fine filtering relying on intrinsic attributes of exact keypoint-matches is designed to more effectively reduce the false keypoint-matches; iv) the adaptive block filling relying on true keypoint-matches contributes to the accurate and efficient suspicious region filling, even at the pixel level. Finally, the suspicious region locations with the forgery vision persistence concept indicate forgery videos. Compared to the state-of-art methods, the experiments show that our proposed method achieves the best detection accuracy, lowest F P , and improved at least 16% and 8% of F 1 scores on the GRIP 2.0 dataset and a combination of SULFA 2.0 & REWIND datasets. Furthermore, the proposed method is with low computational time (4.45 s/Mpixels), which is about 1/2-1/3 times of the latest DFMI-BM (8.02 s/Mpixels) and PM-2D (13.1 s/Mpixels) methods. [ABSTRACT FROM AUTHOR]