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The measurement of quasi-static 3-D knee joint movement is an important basis for studying the mechanism of knee joint injury. Most of the existing measurement methods make use of computed tomography (CT) and nuclear magnetic resonance (MR) imaging technology and hence have the disadvantages of invasiveness, ionizing radiation, low accuracy, and high cost. To overcome those drawbacks, this article innovatively proposes a 3-D motion measurement system for the knee joint based on the registration of CT images to ultrasound (US) images. More specifically, the lower limbs of a subject were first scanned once to acquire the CT images. A portable handheld device was designed to control a US probe for mechanically scanning the subject’s lower limbs in a linear trajectory. During the movement of the subject’s lower limbs, the US scanning was performed quasi-statically. The acquired US images were then registered to the CT images, and the 3-D motions of the lower limb bones could be recreated with the bones scanned in CT images. To guarantee the registration accuracy and efficiency, we used the H-shaped multiview slice assembly as the structural image content for the registration process. The experimental results show that our approach can accurately measure the 3-D motion of the knee joint and meet the needs of 3-D motion analysis of knee joint in practice. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, and the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named attentive encoder–decoder networks for video summarization (AVS), in which the encoder uses a bidirectional long short-term memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on two video summarization benchmark datasets, i.e., SumMe and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches, with remarkable improvements on both datasets. [ABSTRACT FROM AUTHOR]

Breast cancer is now considered as one of the leading causes of deaths among women all over the world. Aiming to assist clinicians in improving the accuracy of diagnostic decisions, computer-aided diagnosis (CAD) system is of increasing interest in breast cancer detection and analysis nowadays. In this paper, a novel computer-aided diagnosis scheme with human-in-the-loop is proposed to help clinicians identify the benign and malignant breast tumors in ultrasound. In this framework, feature acquisition is performed by a user-participated feature scoring scheme that is based on Breast Imaging Reporting and Data System (BI-RADS) lexicon and experience of doctors. Biclustering mining is then used as a useful tool to discover the column consistency patterns on the training data. The patterns frequently appearing in the tumors with the same label can be regarded as a potential diagnostic rule. Subsequently, the diagnostic rules are utilized to construct component classifiers of the Adaboost algorithm via a novel rules combination strategy which resolves the problem of classification in different feature spaces (PC-DFS). Finally, the AdaBoost learning is performed to discover effective combinations and integrate them into a strong classifier. The proposed approach has been validated using a large ultrasounic dataset of 1,062 breast tumor instances (including 418 benign cases and 644 malignant cases) and its performance was compared with several conventional approaches. The experimental results show that the proposed method yielded the best prediction performance, indicating a good potential in clinical applications. [ABSTRACT FROM AUTHOR]

Magnetic resonance elastography (MRE) offers a noninvasive solution to visualize the mechanical properties of soft tissue, but the study suffers from expensive magnetic resonance scanning. Moreover, translating MRE wave images into soft tissue elasticity is a nontrivial issue for clinical professionals and healthcare practitioners. An interactive system—OpenMRE—is thus developed with the aid of ImageJ for numerical MRE study. It is comprised of two comparatively independent toolkits, namely MREA for simulation and MREP for interpretation. MREA mainly deals with the forward problem of MRE, and provides a numerical platform to determine the propagation and distribution of specially designed elastic wave. It is possible to numerically study some state-of-the-art paradigms including multisource and multifrequency MRE. The resultant wave images are interpretable in MREP that is designed for the inverse problem of MRE. It consists of the algorithms for phase unwrapping, directional filtering, and elasticity reconstruction. In a word, OpenMRE offers the MRE community a convenient and well-functioning system for interactive MRE study. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental problems: 1) learning discriminative model parameters and 2) avoiding over-fitting, which often occurs in regression-based classification tasks. We formulate these two problems in terms of relaxing both the strict binary label matrix and graph regularization term into more flexible forms so that the margins between different classes are enlarged as much as possible and the problem of over-fitting is avoided to some extent. This task is accomplished by the proposed double relaxed regression (DRR) method. The convex problem of DRR is solved efficiently with an iterative procedure. Extensive experiments on synthetic and real world image data sets demonstrate the effectiveness of the proposed method in terms of both classification accuracy and running time. [ABSTRACT FROM AUTHOR]

Detecting coherent groups is fundamentally important for crowd behavior analysis. In the past few decades, plenty of works have been conducted on this topic, but most of them have limitations due to the insufficient utilization of crowd properties and the arbitrary processing of individuals. In this study, a Multiview-based Parameter Free framework (MPF) is proposed. Based on the L1-norm and L2-norm, we design two versions of the multiview clustering method, which is the main part of the proposed framework. This paper presents the contributions on three aspects: (1) a new structural context descriptor is designed to characterize the structural properties of individuals in crowd scenes; (2) a self-weighted multiview clustering method is proposed to cluster feature points by incorporating their orientation and context similarities; and (3) a novel framework is introduced for group detection, which is able to determine the group number automatically without any parameter or threshold to be tuned. The effectiveness of the proposed framework is evaluated on real-world crowd videos, and the experimental results show its promising performance on group detection. In addition, the proposed multiview clustering method is also evaluated on a synthetic dataset and several standard benchmarks, and its superiority over the state-of-the-art competitors is demonstrated. [ABSTRACT FROM AUTHOR]

In the past few years, scene contextual information has been increasingly used for action understanding with promising results. However, unsupervised video action clustering using context has been less explored, and existing clustering methods cannot achieve satisfactory performances. In this paper, we propose a novel unsupervised video action clustering method by using the motion-scene interaction constraint (MSIC). The proposed method takes the unique static scene and dynamic motion characteristics of video action into account, and develops a contextual interaction constraint model under a self-representation subspace clustering framework. First, the complementarity of multi-view subspace representation in each context is explored by single-view and multi-view constraints. Afterward, the context-constrained affinity matrix is calculated and the MSIC is introduced to mutually regularize the disagreement of subspace representation in scene and motion. Finally, by jointly constraining the complementarity of multi-views and the consistency of multi-contexts, an overall objective function is constructed to guarantee the video action clustering result. The experiments on four video benchmark datasets (Weizmann, KTH, UCFsports, and Olympic) demonstrate that the proposed method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Sparse unmixing has become a popular tool for hyperspectral imagery interpretation. It refers to finding the optimal subset of a spectral library to reconstruct the image data and further estimate the proportions of different materials. Recently, multi-objective based sparse unmixing methods have presented promising performance because of their advantages in addressing combinatorial problems. A spectral and multi-objective based sparse unmixing (SMoSU) algorithm was proposed in our previous work, which solves the decision-making problem well. However, it does not show outstanding advantages in strong noise cases. To solve the problem, in this paper, SMoSU is improved based on the estimation of distribution algorithms (EDAs). The machine learning based EDAs have been a reliable approach in solving multi-objective problems. However, most of them are for special problems and relatively weak in theoretical foundations. Thus, it is unreliable to extend it directly to sparse unmixing. Here, we improve EDA on the basis of classification and propose a classification-based model for individual generating under the framework of SMoSU (CM-MoSU). In CM-MoSU, the whole population is divided to be positive and negative. Then, the macroinformation of positive individuals is used to guide the generation of new individuals. Therefore, the optimization task could pay more attention to the feasible space with high quality. Moreover, some theoretical analyses are presented to prove the reliability of CM-MoSU. In experiments, several state-of-the-art sparse unmixing algorithms are compared. Both synthetic and real-world experiments demonstrate the effectiveness of CM-MoSU. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) clustering is very important in remote sensing applications. However, most graph-based clustering models are not suitable for dealing with large HSI due to their computational bottlenecks: the construction of the similarity matrix $\boldsymbol {W}$ , the eigenvalue decomposition of the graph Laplacian matrix $\boldsymbol {L}$ , and $k$ -means or other discretization procedures. To solve this problem, we propose a novel approach, scalable graph-based clustering with nonnegative relaxation (SGCNR), to cluster the large HSI. The proposed SGCNR algorithm first constructs an anchor graph and then adds the nonnegative relaxation term. With this, the computational complexity can be reduced to $O(nd\log m+nK^{2}+nKc+K^{3})$ , compared with traditional graph-based clustering algorithms that need at least $O(n^{2}d+n^{2}K)$ or $O(n^{2}d+n^{3})$ , where $n$ , $d$ , $m$ , $K$ , and $c$ are, respectively, the number of samples, features, anchors, classes, and nearest neighbors. In addition, the SGCNR algorithm can directly obtain the clustering indicators, without resort to $k$ -means or other discretization procedures as traditional graph-based clustering algorithms have to do. Experimental results on several HSI data sets have demonstrated the efficiency and effectiveness of the proposed SGCNR algorithm. [ABSTRACT FROM AUTHOR]

Extended field-of-view ultrasound (US EFOV) imaging is a technique used extensively in the clinical field to attain interpretable panorama of anatomy; 2.5-D US EFOV has recently been proposed for spine imaging. In the original 2.5-D US EFOV, it makes use of a six degrees-of-freedom positional sensor attached to the US probe to record the corresponding position of each B-scan. By combining the positional information and the B-scan images, the 2.5-D EFOV can reconstruct a panorama on a curved image plane when the scanning trajectory of the US probe is curved. In this paper, an improved method based on the Bezier interpolation is proposed to better reconstruct 2.5-D US EFOV imaging, producing the panoramas with smoother texture and higher quality. To make it more applicable for scoliosis patients, we designed a novel method called double-sweep 2.5-D EFOV to better image the spinal tissues and easily compute the Cobb angle. In vitro and in vivo experiments demonstrated that the 2.5-D EFOV images obtained by the proposed method can present anatomical structures of the scanning region accurately. [ABSTRACT FROM AUTHOR]

As one of the most widely used clustering techniques, the fuzzy $k$ -means (FKM) assigns every data point to each cluster with a certain degree of membership. However, conventional FKM approach relies on the square data fitting term, which is sensitive to the outliers with ignoring the prior information. In this paper, we develop a novel and robust fuzzy $k$ -means clustering algorithm, namely, joint learning of fuzzy $k$ -means and nonnegative spectral clustering with side information. The proposed method combines fuzzy $k$ -means and nonnegative spectral clustering into a unified model, which can further exploit the prior knowledge of data pairs such that both the quality of affinity graph and the clustering performance can be improved. In addition, for the purpose of enhancing the robustness, the adaptive loss function is adopted in the objective function, since it smoothly interpolates between $\ell _{1}$ -norm and $\ell _{2}$ -norm. Finally, experimental results on benchmark datasets verify the effectiveness and the superiority of our clustering method. [ABSTRACT FROM AUTHOR]

Sparse regression based feature selection method has been extensively investigated these years. However, because it has a non-convex constraint, i.e., $\ell _{2,0}$ℓ2,0-norm constraint, this problem is very hard to solve. In this paper, unlike most of the other methods which only solve its slack version by introducing sparsity regularization into objective function forcibly, a novel framework is proposed by us to solve the original $\ell _{2,0}$ℓ2,0-norm constrained sparse regression based feature selection problem. We transform our objective function into Linear Discriminant Analysis (LDA) by using a new label coding method, thus enabling our model to calculate the ratio of inter-class scatter to intra-class scatter of features which is the most widely used feature discrimination evaluation metric. According to that ratio, features can be selected by a simple sorting method. The projection gradient descent algorithm is introduced to further improve the performance of our algorithm by using the solution obtained before as its initial solution. This ensures the stability of this iterative algorithm. We prove that the proposed method can get the global optimal solution of this non-convex problem when all features are statistically independent. For the general case where features are statistically dependent, extensive experiments on six small sample size datasets and one large-scale dataset show that our algorithm has comparable or better classification capability comparing with other eight state-of-the-art feature selection methods by the SVM classifier. We also show that our algorithm can obtain a low loss value, which means the solution of our algorithm can get very close to this NP-hard problem's real solution. What is more, because we solve the original $\ell _{2,0}$ℓ2,0-norm constrained problem, we avoid the heavy work of tuning the regularization parameter because its meaning is explicit in our method, i.e., the number of selected features. At last, we evaluate the stability of our algorithm from two perspectives, i.e., the objective function values and the selected features, by experiments. From both perspectives, our algorithm shows satisfactory stability performance. [ABSTRACT FROM AUTHOR]

2-D linear discriminant analysis (2DLDA) has been widely used in pattern recognition and image classification. 2DLDA selects discriminative features from the up and left corner of images. However, 2DLDA uses the Frobenius norm (F-norm), which is sensitive to noise or outliers in data, as a metric. In this paper, we propose a novel framework, called horizontal and vertical nuclear norm-based 2DLDA (HVNN-2DLDA) for image representation. In the proposed framework, HVNN-2DLDA methods (i.e., HNN-2DLDA and VNN-2DLDA) are proposed, and both use the nuclear norm as a criterion. The nuclear norm can provide more structure and global information for the reconstruction of noisy images. HNN-2DLDA and VNN-2DLDA represent images in the row and column directions, respectively. In addition, by combining the row and column directions, we propose a bilateral nuclear norm-based 2DLDA method called BNN-2DLDA. The advantage of BNN-2DLDA over HNN-2DLDA and VNN-2DLDA is that an image sample can be represented by both the row and the column directions instead of only the row or column direction. HVNN-2DLDA learns a set of local optimal projection vectors by maximizing the ratio of the nuclear norm of the between-class scatter matrix and the nuclear norm of the within-class scatter matrix. To verify the robustness and recognition performance in image classification of HVNN-2DLDA, six public image databases are used for experiments. The experimental results demonstrate the effectiveness and the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Dynamic scene classification has been extensively studied in computer vision due to its widespread applications. The key to dynamic scene classification lies in jointly characterizing spatial appearance and temporal dynamics to achieve informative representation, which remains an outstanding task in the literature. In this paper, we propose a unified framework to extract spatial and temporal features for dynamic scene representation. More specifically, we deploy two variants of deep convolutional neural networks to encode spatial appearance and short-term dynamics into short-term deep features (STDF). Based on STDF, we propose using the autoregressive moving average model to extract long-term frequency features (LTFF). By combining STDF and LTFF, we establish the long–short-term feature (LSTF) representations of dynamic scenes. The LSTF characterizes both spatial and temporal patterns of dynamic scenes for comprehensive and information representation that enables more accurate classification. Extensive experiments on three-dynamic scene classification benchmarks have shown that the proposed LSTF achieves high performance and substantially surpasses the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Scene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In the field of 3-D technology, it is interesting as well as meaningful issue to control object depth in 3-D space. Recently, some depth control methods for stereoscopic images have been proposed, which usually employ depth map or directly process color images to implement depth control. There are two main disadvantages for these methods. First, the results of these methods usually suffer from object deformation and holes. Second, these methods are prone to cause undesired object size changing in 3-D space. To address these issues, we propose a shape-preserving object depth control method for stereoscopic images. First, a novel depth mapping model is presented for calculating the ideal coordinates of the key points in depth control, so that the shape of the object can be well preserved. Afterward, the image content-based constraints are used to further preserve the structure of the object and its background. Finally, the warping technology is introduced to deal with images optimally as well as to avoid holes. Experimental results show that the proposed method can control object depth and preserve the shape of the object effectively without sensible background distortion. [ABSTRACT FROM AUTHOR]

As a popular dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in image classification. However, the NMF does not consider discriminant information from the data themselves. In addition, most NMF-based methods use the Euclidean distance as a metric, which is sensitive to noise or outliers in data. To solve these problems, in this paper, we introduce structural incoherence and low-rank to NMF and propose a novel nonnegative factorization method, called structurally incoherent low-rank NMF (SILR-NMF), in which we jointly consider structural incoherence and low-rank properties of data for image classification. For the corrupted data, we use the $L_{1}$ norm as a constraint to ensure the noise is sparse. SILR-NMF learns a clean data matrix from the noisy data by low-rank learning. As a result, the SILR-NMF can capture the global structure information of the data, which is more robust than local information to noise. By introducing the structural incoherence of the learned clean data, SILR-NMF ensures the clean data points from different classes are as independent as possible. To verify the performance of the proposed method, extensive experiments are conducted on six image databases. The experimental results demonstrate that our proposed method has substantial gain over existing NMF approaches. [ABSTRACT FROM AUTHOR]

Sufficient training examples are the fundamental requirement for most of the learning tasks. However, collecting well-labelled training examples is costly. Inspired by Zero-shot Learning (ZSL) that can make use of visual attributes or natural language semantics as an intermediate level clue to associate low-level features with high-level classes, in a novel extension of this idea, we aim to synthesise training data for novel classes using only semantic attributes. Despite the simplicity of this idea, there are several challenges. First, how to prevent the synthesised data from over-fitting to training classes? Second, how to guarantee the synthesised data is discriminative for ZSL tasks? Third, we observe that only a few dimensions of the learnt features gain high variances whereas most of the remaining dimensions are not informative. Thus, the question is how to make the concentrated information diffuse to most of the dimensions of synthesised data. To address the above issues, we propose a novel embedding algorithm named Unseen Visual Data Synthesis (UVDS) that projects semantic features to the high-dimensional visual feature space. Two main techniques are introduced in our proposed algorithm. (1) We introduce a latent embedding space which aims to reconcile the structural difference between the visual and semantic spaces, meanwhile preserve the local structure. (2) We propose a novel Diffusion Regularisation (DR) that explicitly forces the variances to diffuse over most dimensions of the synthesised data. By an orthogonal rotation (more precisely, an orthogonal transformation), DR can remove the redundant correlated attributes and further alleviate the over-fitting problem. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data for zero-shot learning. Extensive experimental results suggest that our proposed approach significantly outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, notable progress has been made in scene classification and target detection. However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal. [ABSTRACT FROM AUTHOR]

Traffic videos are often recorded by vehicle-mounted cameras. Compared with videos recorded by handheld cameras, traffic videos suffer from more challenges, such as higher frequency and more violent jitters, dynamic scenes, large moving objects, and parallax, which can result in significant visual quality degradation. To address these challenges for traffic videos, we propose a special stabilization method. The key aspect of our method is a feedback strategy that divides the extracted feature trajectories into background trajectories and foreground trajectories by feeding back the previous trajectory classification results. The method can perform robustly, even in the case of large moving objects and parallax. Furthermore, our method maintains the number of available background trajectories within a reasonable range via two refinement strategies. One strategy attempts to reliably recover background trajectories from misjudged foreground trajectories when there are an insufficient number of background trajectories. The other strategy can adaptively adjust the number of feature points in each frame to efficiently avoid too many or too few background trajectories. With the obtained background trajectories, a homography matrix between each frame and its stabilized view is computed and implemented to warp the frame image to produce smooth videos. Experimental results confirm that our method is both effective in stabilizing traffic videos and quite robust against large moving objects and parallax. [ABSTRACT FROM PUBLISHER]

With the increasing popularity of traffic-intensive video applications, UDNs are treated as one of the most promising technologies for massive video delivery. However, due to the drastic interference between neighboring cells, how to achieve high energy and spectrum efficiency is still an open and challenging problem. This work proposes a systematic solution for content delivery over UDNs by integrating collaboration with intelligence. In particular, we first design a hybrid video coding scheme that is flexible and robust to the dynamic wireless environment. Then an active and proactive video update strategy is designed by intelligently alleviating the impact of the interference. Finally, a collaborative video scheduling scheme is developed to maximize the video quality as well as the energy and spectrum efficiency. Importantly, we summarize three fundamental design guidelines, and believe that they are useful for improving the transmission capacity of UDNs. [ABSTRACT FROM PUBLISHER]

Modeling the process of information diffusion is a challenging problem. Although numerous attempts have been made in order to solve this problem, very few studies are actually able to simulate and predict temporal dynamics of the diffusion process. In this paper, we propose a novel information diffusion model, namely GT model, which treats the nodes of a network as intelligent and rational agents and then calculates their corresponding payoffs, given different choices to make strategic decisions. By introducing time-related payoffs based on the diffusion data, the proposed GT model can be used to predict whether or not the user's behaviors will occur in a specific time interval. The user’s payoff can be divided into two parts: social payoff from the user’s social contacts and preference payoff from the user’s idiosyncratic preference. We here exploit the global influence of the user and the social influence between any two users to accurately calculate the social payoff. In addition, we develop a new method of presenting social influence that can fully capture the temporal dynamics of social influence. Experimental results from two different datasets, Sina Weibo and Flickr demonstrate the rationality and effectiveness of the proposed prediction method with different evaluation metrics. [ABSTRACT FROM PUBLISHER]

Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby leading to severe information loss and performance degradation. In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data. We propose a novel spectral clustering scheme which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions. Specifically, we explicitly enforce a discrete transformation on the intermediate continuous labels, which leads to a tractable optimization problem with a discrete solution. Besides, we preserve the natural nonnegative characteristic of the clustering labels to enhance the interpretability of the results. Moreover, to further compensate the unreliability of the learned clustering labels, we integrate an adaptive robust module with \ell 2,p loss to learn prediction function for grouping unseen data. We also show that the out-of-sample component can inject discriminative knowledge into the learning of cluster labels under certain conditions. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to several existing clustering approaches. [ABSTRACT FROM PUBLISHER]

We study content-based learning to rank from the perspective of learning distance functions. Standardly, the two key issues of learning to rank, feature mappings and score functions, are usually modeled separately, and the learning is usually restricted to modeling a linear distance function such as the Mahalanobis distance. However, the modeling of feature mappings and score functions are mutually interacted, and the patterns underlying the data are probably complicated and nonlinear. Thus, as a general nonlinear distance family, the Bregman distance is a suitable distance function for learning to rank, due to its strong generalization ability for distance functions, and its nonlinearity for exploring the general patterns of data distributions. In this paper, we study learning to rank as a structural learning problem, and devise a Bregman distance function to build the ranking model based on structural SVM. To improve the model robustness to outliers, we develop a robust structural learning framework for the ranking model. The proposed model Robust Structural Bregman distance functions Learning to Rank (RSBLR) is a general and unified framework for learning distance functions to rank. The experiments of data ranking on real-world datasets show the superiority of this method to the state-of-the-art literature, as well as its robustness to the noisily labeled outliers. [ABSTRACT FROM PUBLISHER]

Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification. NDMF integrates the nonnegative constraint, orthogonality, and discriminant information in the objective function. NDMF considers the incoherent information of both factors in standard NMF and is proposed to enhance the discriminant ability of the learned base matrix. NDMF projects the low-dimensional representation of the subspace of the base matrix to regularize the NMF for discriminant subspace learning. Based on the Euclidean distance metric and the generalized Kullback–Leibler (KL) divergence, two kinds of iterative algorithms are presented to solve the optimization problem. The between- and within-class scatter matrices are divided into positive and negative parts for the update rules and the proofs of the convergence are also presented. Extensive experimental results demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art discriminant NMF algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate \mathcal O(1/\lambda T) , but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain \mathcal O(\sqrt\log (1/\delta)/T) with $\delta$ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately \mathcal {O}(\log (\log (T)/\delta)/\lambda T) . [ABSTRACT FROM AUTHOR]

Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to learn compact binary codes that construct the underlying correlations between heterogeneous features from different modalities. A majority of recent approaches aim at learning hash functions to preserve the pairwise similarities defined by given class labels. However, these methods fail to explicitly explore the discriminative property of class labels during hash function learning. In addition, they usually discard the discrete constraints imposed on the to-be-learned binary codes, and compromise to solve a relaxed problem with quantization to obtain the approximate binary solution. Therefore, the binary codes generated by these methods are suboptimal and less discriminative to different classes. To overcome these drawbacks, we propose a novel cross-modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints. Specifically, DCH learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes. Extensive experiments on three benchmark data sets highlight the superiority of DCH under various cross-modal scenarios and show its state-of-the-art performance. [ABSTRACT FROM PUBLISHER]

Heterogeneous face recognition is an important, yet challenging problem in face recognition community. It refers to matching a probe face image to a gallery of face images taken from alternate imaging modality. The major challenge of heterogeneous face recognition lies in the great discrepancies between different image modalities. Conventional face feature descriptors, e.g., local binary patterns, histogram of oriented gradients, and scale-invariant feature transform, are mostly designed in a handcrafted way and thus generally fail to extract the common discriminant information from the heterogeneous face images. In this paper, we propose a new feature descriptor called common encoding model for heterogeneous face recognition, which is able to capture common discriminant information, such that the large modality gap can be significantly reduced at the feature extraction stage. Specifically, we turn a face image into an encoded one with the encoding model learned from the training data, where the difference of the encoded heterogeneous face images of the same person can be minimized. Based on the encoded face images, we further develop a discriminant matching method to infer the hidden identity information of the cross-modality face images for enhanced recognition performance. The effectiveness of the proposed approach is demonstrated (on several public-domain face datasets) in two typical heterogeneous face recognition scenarios: matching NIR faces to VIS faces and matching sketches to photographs. [ABSTRACT FROM PUBLISHER]

Device-to-device (D2D) communications have recently attracted much attention for the potential capability to improve spectral efficiency (SE) underlaying the existing heterogeneous networks (HetNets). Due to no sophisticated control, D2D-worked user equipments (DUEs) themselves cannot resist eavesdropping or security attacks. It is urgent to maximize the secure capacity for both cellular users and DUEs. This paper formulates the radio resource-allocation problem to maximize the secure capacity of DUEs for D2D communication underlaying HetNets, which consist of high-power nodes (HPNs) and low-power nodes (LPNs). The optimization objective function with transmit bit rate and power constraints, which is nonconvex and hard to directly derive, is first transformed into a matrix form. Then, the equivalent convex form of the optimization problem is derived according to Perron–Frobenius theory. A heuristic iterative algorithm based on the proximal theory is proposed to solve this equivalent convex problem through evaluating the proximal operator of the Lagrange function. Numerical results show that the proposed radio resource-allocation solution significantly improves the secure capacity with a fast convergence speed. [ABSTRACT FROM PUBLISHER]

With the rapid development of the mobile service and online social networking service, a large number of mobile images are generated and shared on the social networks every day. The visual content of these images contains rich knowledge for many uses, such as social categorization and recommendation. Mobile image labeling has, therefore, been proposed to understand the visual content and received intensive attention in recent years. In this paper, we present a novel mobile image labeling scheme on the cloud, in which mobile images are first and efficiently transmitted to the cloud by Hamming compressed sensing, such that the heavy computation for image understanding is transferred to the cloud for quick response to the queries of the users. On the cloud, we design a sparse correntropy framework for robustly learning the semantic content of mobile images, based on which the relevant tags are assigned to the query images. The proposed framework (called maximum correntropy-based mobile image labeling) is very insensitive to the noise and the outliers, and is optimized by a half-quadratic optimization technique. We theoretically show that our image labeling approach is more robust than the squared loss, absolute loss, Cauchy loss, and many other robust loss function-based sparse coding methods. To further understand the proposed algorithm, we also derive its robustness and generalization error bounds. Finally, we conduct experiments on the PASCAL VOC’07 data set and empirically demonstrate the effectiveness of the proposed robust sparse coding method for mobile image labeling. [ABSTRACT FROM PUBLISHER]