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Abstract: Non-rigid 3D shape retrieval has become an active and important research topic in content-based 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site . [Copyright &y& Elsevier]

Abstract: Human hand shape features extraction from image frame sequences is one of the key steps in human hand 2D/3D tracking system and human hand shape recognition system. In order to satisfy the need of human hand tracking in real time, a fast and accurate method for acquirement of edge features from human hand images without consideration of hand over face is put forward in this paper. The proposed approach is composed of two steps, the coarse location phase (CLP) and the refined location phase (RLP) from coarseness to refinement. In the phase of CLP, the hand contour is approximately described by a polygon with concave and convex, an approach to obtaining hand shape polygon using locating points and locating lines is meticulously discussed. Then, a coarse location (CL) algorithm for extraction of interested hand shape features, such as contour, fingertips, roots of fingers, joints and the intersection of knuckle on different fingers, is proposed. In the phase of RLP, a multi-scale approach is introduced into our study to refine the features obtained by the CL algorithm. By means of defining the response strength of different types of features, a refined location (RL) algorithm is proposed. The major contribution of this paper is that the novel detection operators for features of hand images are presented in the above two steps, which have been successfully applied to our 3D hand shape tracking system and 2D hand shape recognition system. A number of comparative studies with real images and online videos demonstrate that the proposed method can extract the three defined human hand image features with high accuracy and high speed. [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]

Abstract: Multi-label core vector machine (Rank-CVM) is an efficient and effective algorithm for multi-label classification. But there still exist two aspects to be improved: reducing training and testing computational costs further, and detecting relevant labels effectively. In this paper, we extend Rank-CVM via adding a zero label to construct its variant with a zero label, i.e., Rank-CVMz, which is formulated as the same quadratic programming form with a unit simplex constraint and non-negative ones as Rank-CVM, and then is solved by Frank–Wolfe method efficiently. Attractively, our Rank-CVMz has fewer variables to be solved than Rank-CVM, which speeds up training procedure dramatically. Further, the relevant labels are effectively detected by the zero label. Experimental results on 12 benchmark data sets demonstrate that our method achieves a competitive performance, compared with six existing multi-label algorithms according to six indicative instance-based measures. Moreover, on the average, our Rank-CVMz runs 83 times faster and has slightly fewer support vectors than its origin Rank-CVM. [Copyright &y& Elsevier]

Abstract: This paper submits a comprehensive report of the use of order statistics (OS) for parametric pattern recognition (PR) for various distributions within the exponential family. Although the field of parametric PR has been thoroughly studied for over five decades, the use of the OS of the distributions to achieve this has not been reported. The pioneering work on using OS for classification was presented earlier for the uniform distribution and for some members of the exponential family, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean. Apart from the results for the Gaussian and doubly exponential which are merely cited here, our new results include the Rayleigh, Gamma and certain Beta distributions. The new scheme, referred to as classification by moments of order statistics (CMOS), has an accuracy that attains Bayes' bound for symmetric distributions, and is, otherwise, very close to the optimal Bayes' bound, as has been shown both theoretically and by rigorous experimental testing. The results here also give a theoretical foundation for the families of border identification (BI) algorithms reported in the literature. [Copyright &y& Elsevier]

Abstract: In linear representation based face recognition (FR), it is expected that a discriminative dictionary can be learned from the training samples so that the query sample can be better represented for classification. On the other hand, dimensionality reduction is also an important issue for FR. It cannot only reduce significantly the storage space of face images, but also enhance the discrimination of face feature. Existing methods mostly perform dimensionality reduction and dictionary learning separately, which may not fully exploit the discriminative information in the training samples. In this paper, we propose to learn jointly the projection matrix for dimensionality reduction and the discriminative dictionary for face representation. The joint learning makes the learned projection and dictionary better fit with each other so that a more effective face classification can be obtained. The proposed algorithm is evaluated on benchmark face databases in comparison with existing linear representation based methods, and the results show that the joint learning improves the FR rate, particularly when the number of training samples per class is small. [Copyright &y& Elsevier]

Abstract: In the “bag of visual words (BoVW)” representation each image is represented by an unordered set of visual words. In this paper, a novel approach to encode ordered spatial configurations of visual words in order to add context in the representation is presented. The proposed method introduces a bag of spatio-visual words representation (BoSVW) obtained by clustering of visual words'' correlogram ensembles. Specifically, the spherical K-means clustering algorithm is employed accounting for the large dimensionality and the sparsity of the proposed spatio-visual descriptors. Experimental results on four standard datasets show that the proposed method significantly improves a state-of-the-art BoVW model and compares favorably to existing context-based scene classification approaches. [Copyright &y& Elsevier]

Abstract: Feature interaction is an important issue in feature subset selection. However, most of the existing algorithms only focus on dealing with irrelevant and redundant features. In this paper, a propositional FOIL rule based algorithm FRFS, which not only retains relevant features and excludes irrelevant and redundant ones but also considers feature interaction, is proposed for selecting feature subset for high dimensional data. FRFS first merges the features appeared in the antecedents of all FOIL rules, achieving a candidate feature subset which excludes redundant features and reserves interactive ones. Then, it identifies and removes irrelevant features by evaluating features in the candidate feature subset with a new metric CoverRatio, and obtains the final feature subset. The efficiency and effectiveness of FRFS are extensively tested upon both synthetic and real world data sets, and it is compared with other six representative feature subset selection algorithms, including CFS, FCBF, Consistency, Relief-F, INTERACT, and the rule-based FSBAR, in terms of the number of selected features, runtime and the classification accuracies of the four well-known classifiers including Naive Bayes, C4.5, PART and IB1 before and after feature selection. The results on the five synthetic data sets show that FRFS can effectively identify irrelevant and redundant features while reserving interactive ones. The results on the 35 real world high dimensional data sets demonstrate that compared with other six feature selection algorithms, FRFS cannot only efficiently reduce the feature space, but also can significantly improve the performance of the four well-known classifiers. [Copyright &y& Elsevier]

Abstract: The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cluster validity indices in many different environments with different characteristics. These results can serve as a guideline for selecting the most suitable index for each possible application and provide a deep insight into the performance differences between the currently available indices. [Copyright &y& Elsevier]

Abstract: This paper presents a supervised learning algorithm for image deblurring. The task is addressed into the conceptual framework of matrix regression and gradient evolution. Specifically, given pairs of blurred image patches and their corresponding clear ones, an optimization framework of matrix regression is proposed to learn a matrix mapping. For an image to be deblurred, the learned matrix mapping will be employed to map each of its image patches directly to be a new one with more sharp details. The mapped result is then analyzed in terms of edge profiles, and the image is finally deblurred in way of gradient evolution. The algorithm is fast, and easy to be implemented. Comparative experiments on diverse natural images and the applications to interactive deblurring of real-world out-of-focus images illustrate the validity of our method. [Copyright &y& Elsevier]

Abstract: In this paper we address the problem of robust face recognition by formulating the pattern recognition task as a problem of robust estimation. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace (Barsi and Jacobs, 2003 ), we develop a linear model representing a probe image as a linear combination of class specific galleries. In the presence of noise, the well-conditioned inverse problem is solved using the robust Huber estimation and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Robust Linear Regression Classification (RLRC) algorithm is extensively evaluated for two important cases of robustness i.e. illumination variations and random pixel corruption. Illumination invariant face recognition is demonstrated on three standard databases under exemplary evaluation protocols reported in the literature. Comprehensive comparative analysis with the state-of-art illumination tolerant approaches indicates a comparable performance index for the proposed RLRC algorithm. The efficiency of the proposed approach in the presence of severe random noise is validated under several exemplary noise models such as dead-pixel problem, salt and pepper noise, speckle noise and Additive White Gaussian Noise (AWGN). The RLRC algorithm is found to be favorable compared with the benchmark generative approaches. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a mechanism for systematic comparison of the efficacy of unsupervised evaluation methods for parameter selection of binarization algorithms in optical character recognition (OCR). We also analyze these measures statistically and ascertain whether a measure is suitable or not to assess a binarization method. The comparison process is streamlined in several steps. Given an unsupervised measure and a binarization algorithm we: (i) find the best parameter combination for the algorithm in terms of the measure, (ii) use the best binarization of an image on an OCR, and (iii) evaluate the accuracy of the characters detected. We also propose a new unsupervised measure and a statistical test to compare measures based on an intuitive triad of possible results: better, worse or comparable performance. The comparison method and statistical tests can be easily generalized for new measures, binarization algorithms and even other accuracy-driven tasks in image processing. Finally, we perform an extensive comparison of several well known measures, binarization algorithms and OCRs, and use it to show the strengths of the WV measure. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost. [Copyright &y& Elsevier]

Abstract: Generally, collecting a large quantity of unlabeled examples is feasible, but labeling them all is not. Active learning can reduce the number of labeled examples needed to train a good classifier. Existing active learning algorithms can be roughly divided into three categories: single-view single-learner (SVSL) active learning, multiple-view single-learner (MVSL) active learning and single-view multiple-learner (SVML) active learning. In this paper, a new approach that incorporates multiple views and multiple learners (MVML) into active learning is proposed. Multiple artificial neural networks are used as learners in each view, and they are set with different numbers of hidden neurons and weights to ensure each of them has a different bias. The selective sampling of our proposed method is implemented in three different ways. For comparative purpose, the traditional methods MVSL and SVML active learning as well as bagging active learning and adaboost active learning are also implemented together with MVML active learning in our experiments. The empirical results indicate that the MVML active learning outperforms the other traditional methods. [Copyright &y& Elsevier]