Discriminative Sparse Representation Research Articles

Augmented Sparse Representation for Incomplete Multiview Clustering.

Incomplete multiview data are collected from multiple sources or characterized by multiple modalities, where the features of some samples or some views may be missing. Incomplete multiview clustering (IMVC) aims to partition the data into different groups by taking full advantage of the complementary information from multiple incomplete views. Most existing methods based on matrix factorization or subspace learning attempt to recover the missing views or perform imputation of the missing features to improve clustering performance. However, this problem is intractable due to a lack of prior knowledge, e.g., label information or data distribution, especially when the missing views or features are completely damaged. In this article, we proposed an augmented sparse representation (ASR) method for IMVC. We first introduce a discriminative sparse representation learning (DSRL) model, which learns the sparse representations of multiple views as applied to measure the similarity of the existing features. The DSRL model explores complementary and consistent information by integrating the sparse regularization item and a consensus regularization item, respectively. Simultaneously, it learns a discriminative dictionary from the original samples. The sparsity constrained optimization problem in the DSRL model can be efficiently solved by the alternating direction method of multipliers (ADMM). Then, we present a similarity fusion scheme, namely, a sparsity augmented fusion of sparse representations, to obtain a sparsity augmented similarity matrix across different views for spectral clustering. Experimental results on several datasets demonstrate the effectiveness of the proposed ASR method for IMVC.

A new weighted extreme learning machine based on elastic net regularization embedded exponential regularized discriminative dictionary learning for image classification

It is well known that discriminative sparse representation can significantly improve the performance of image classification. However, there remain several tricky issues to be addressed due to the unsatisfied performance and high time consumption. In this paper, a novel classification framework called weighted extreme learning machine exponential regularized discriminative dictionary learning (WELM-ERDDL) is proposed to address these issues. The main contributions of this paper include (1) the WELM is embedded with ERDDL via exponential regularized linear discriminative analysis (ERLDA) for feature mappings while enabling nonlinear and diverse feature representation; (2) in the ELM learning process, the elastic net regularization is utilized to optimize more robust and meaningful output weights; (3) an effective weight update rule is designed for WELM. To verify the effectiveness of the proposed method, several experiments are conducted on real-world image classification databases. The results show that the proposed WELM-ERDDL framework is even more efficient than other state-of-the-art algorithms in general.

Adaptive graph regularized and label embedded dictionary learning for pattern classification

During the last decade, graph regularized dictionary learning (DL) models have obtained a lot of attention due to their flexible and discriminative ability in nonlinear pattern classification. However, the conventional graph-regularized methods construct a fixed affinity matrix for nearby samples in high-dimensional data space, which is vulnerable to noisy and redundant sample features. Furthermore, the discrimination of the graph regularized representation is not fully explored with the supervised classifier learning framework. To remedy these limitations, we propose an adaptive graph-regularized and label-embedded DL model for pattern classification. Especially, the affinity graph construction in low-dimensional representation space and the discriminative sparse representation is simultaneously learned in a unified framework for mutual promotion. More concretely, we iteratively update the sample similarity weight matrix in representation space to enhance the model robustness and further impose a supervised label-embedding term on sparse representation to enhancing its discriminative capability for classification. The dictionary orthonormal constraint is also considered to eliminate the redundant atoms and enhance the model discrimination. An efficient alternating direction solution with guaranteed convergence is proposed for the nonconvex and unsmooth model. Experimental results on five benchmark datasets verify the effectiveness of the proposed model.

Multi‐Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial‐Temporal Constraint

Vehicles or pedestrians tracking is an important task in intelligent transportation system. In this paper, we propose an online multi-object tracking for intelligent traffic platform that employs improved sparse representation and structural constraint. We first build the spatial-temporal constraint via the geometric relations and appearance of tracked objects, then we construct a robust appearance model by incorporating the discriminative sparse representation with weight constraint and local sparse appearance with occlusion analysis. Finally, we complete data association by using maximum a posteriori in a Bayesian framework in the pursuit for the optimal detection estimation. Experimental results in two challenging vehicle tracking benchmark datasets show that the proposed method has a good tracking performance.

No-reference stereo image quality assessment based on discriminative sparse representation

No-reference stereo image quality assessment based on discriminative sparse representation

An efficient dictionary-based multi-view learning method

Multi-view learning can be considered as a kind of classification method which explores common and unique information among different views. For dictionary learning, it can identify informative features by learning sparse representation of samples and has great advantages for classification. However, there are few researches on the problem of multi-view learning with dictionary learning. In order to improve the performance of multi-view classification, we propose a new multi-view dictionary learning with consensus of view(MVDL-CV). First of all, we learn a particular dictionary for each view and obtain the sparse representation of the sample. Then, by utilizing the regularization term between two dictionaries in consensus, we can determine the similarity of samples and obtain the discriminative sparse representation, which can be helpful to construct the improved classifiers. Further, we obtain the solution of the model through an alternating convex optimization method and present the convergence analysis of MVDL-CV. In the experiments, we compare the proposed method with previous multi-view learning methods, and the experimental results show that MVDL-CV is a feasible and competitive method.

Weighted Discriminative Sparse Representation for Image Classification

Sparse representation methods based on $$l _2$$ norm regularization have attracted much attention due to its low computational cost and competitive performance. How to enhance the discriminability of $$l _2$$ norm regularization-based representation method is a meaningful work. In this paper, we put forward a novel $$l _2$$ norm regularization-based representation method, called Weighted Discriminative Sparse Representation for Classification (WDSRC), in which we consider the global discriminability and the local discriminability using two discriminative regularization terms of representation. The global discriminability is obtained by decorrelating the representation results stemming from all distinct classes. The local discriminability is achieved by the weighted representation in which the representation coefficient of the training images dissimilar to the test image will be reduced and the representation coefficient of the training images similar to the test image will be increased, which restrains the training images dissimilar to the test image and promotes the training images similar to the test image as much as possible in representing the test sample. By considering the global and local discriminability of representations simultaneously, the proposed WDSRC method can gain more discriminative representation for classification. Extensive experiments on benchmark datasets of object, face, action and flower demonstrate the effectiveness of the proposed WDSRC method.

A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection.

Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification (SRC) methods have achieved the good performance in EEG signal automatic detection, by which the EEG signals are sparsely represented using a few active coefficients in the dictionary and classified according to the reconstruction criteria. However, most of SRC learn a linear dictionary for encoding, and cannot extract enough information and nonlinear relationship of data for classification. To solve this problem, a hierarchical discriminative sparse representation classification model (called HD-SRC) for EEG signal detection is proposed. Based on the framework of neural network, HD-SRC learns the hierarchical nonlinear transformation and maps the signal data into the nonlinear transformed space. Through incorporating this idea into label consistent K singular value decomposition (LC-KSVD) at the top layer of neural network, HD-SRC seeks discriminative representation together with dictionary, while minimizing errors of classification, reconstruction and discriminative sparse-code for pattern classification. By learning the hierarchical feature mapping and discriminative dictionary simultaneously, more discriminative information of data can be exploited. In the experiment the proposed model is evaluated on the Bonn EEG database, and the results show it obtains satisfactory classification performance in multiple EEG signal detection tasks.

FDSR: A new fuzzy discriminative sparse representation method for medical image classification

Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Despite being a powerful classification tool, the sparse representation suffers from the lack of sufficient discrimination and robustness, which are required to manage the uncertainty and noisiness in medical image classification issues. It is tried to overcome this deficiency by introducing a new fuzzy discriminative robust sparse representation classifier, which benefits from the fuzzy terms in its optimization function of the dictionary learning process. In this work, we present a new medical image classification approach, fuzzy discriminative sparse representation (FDSR). The proposed fuzzy terms increase the inter-class representation difference and the intra-class representation similarity. Also, an adaptive fuzzy dictionary learning approach is used to learn dictionary atoms. FDSR is applied on Magnetic Resonance Images (MRI) from three medical image databases. The comprehensive experimental results clearly show that our approach outperforms its series of rival techniques in terms of accuracy, sensitivity, specificity, and convergence speed.

Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning.

In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL) framework for the discriminative sparse representation (SR) learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective DPL, locality-adaptive SRs, and discriminative coding coefficients learning into a unified learning framework. Specifically, RA-DPL improves existing projective DPL in four perspectives. First, it applies a sparse l2,1 -norm-based metric to encode the reconstruction error to deliver the robust projective dictionary pairs, and the l2,1 -norm has the potential to minimize the error. Second, it imposes the robust l2,1 -norm clearly on the analysis dictionary to ensure the sparse property of the coding coefficients rather than using the costly l0/l1 -norm. As such, the robustness of the data representation and the efficiency of the learning process are jointly considered to guarantee the efficacy of our RA-DPL. Third, RA-DPL conceives a structured reconstruction weight learning paradigm to preserve the local structures of the coding coefficients within each class clearly in an adaptive manner, which encourages to produce the locality preserving representations. Fourth, it also considers improving the discriminating ability of coding coefficients and dictionary by incorporating a discriminating function, which can ensure high intraclass compactness and interclass separation in the code space. Extensive experiments show that our RA-DPL can obtain superior performance over other state of the arts.

ELM embedded discriminative dictionary learning for image classification

Dictionary learning is a widely adopted approach for image classification. Existing methods focus either on finding a dictionary that produces discriminative sparse representation, or on enforcing priors that best describe the dataset distribution. In many cases, the dataset size is often small with large intra-class variability and nondiscriminative feature space. In this work we propose a simple and effective framework called ELM-DDL to address these issues. Specifically, we represent input features with Extreme Learning Machine (ELM) with orthogonal output projection, which enables diverse representation on nonlinear hidden space and task specific feature learning on output space. The embeddings are further regularized via a maximum margin criterion (MMC) to maximize the inter-class variance and minimize intra-class variance. For dictionary learning, we design a novel weighted class specific ℓ1,2 norm to regularize the sparse coding vectors, which promotes uniformity of the sparse patterns of samples belonging to the same class and suppresses support overlaps of different classes. We show that such regularization is robust, discriminative and easy to optimize. The proposed method is combined with a sparse representation classifier (SRC) to evaluate on benchmark datasets. Results show that our approach achieves state-of-the-art performance compared to other dictionary learning methods.

Kernel based locality – Sensitive discriminative sparse representation for face recognition

Kernel-based Sparse Representation (SR) has impacted positively on the classification performance in image recognition and has eradicated the problems attributed to the nonlinear distribution of face images and its implementation with Dictionary Learning (DL). However, the locality construction of image data containing more discriminative information, which is crucial for classification has not been fully examined by the current Kernel Sparse representation-based approaches. Furthermore, similar coding outcomes between test samples and neighbouring training data, restrained in the kernel space are not being fully realized from the image features with similar image groupings to effectively capture the embedded discriminative information. To handle these issues, we propose a novel DL method, Kernel Locality-Sensitive Discriminative SR (K-LSDSR) for face recognition. In the proposed K-LSDSR, a discriminative loss function for the groupings based on sparse coefficients is introduced into a locality-sensitive DL (LSDL). After solving the optimized dictionary, the sparse coefficients for the testing image feature samples are obtained, and then the classification results for face recognition is realized by reducing the error between the original and reassembled samples. Experimental results have shown that the proposed K-LSDSR significantly improves the performance of face recognition accuracies compared with competing methods and is robust to various diverse environments in image recognition.

Discriminative Face Recognition Methods with Structure and Label Information via $$l_2$$-Norm Regularization

Existing sparse representation methods either fail to incorporate the structure and label information of training samples, or suffer from expensive computation for $$l_1$$- or $$l_{2,1}$$-norm. In this paper, we propose three discriminative sparse representation classification methods with structure and label information based on $$l_2$$-norm regularization for robust face recognition. We propose the first classification method with structure and label information by enforcing competition among the representation results of training samples from different classes in representing a test sample. To make the classification more discriminative, we present the decorrelation classification method with structure and label information by jointly considering the competition and decorrelation regularizations. In addition, by incorporating the locality information of samples, we propose the third method called locality-constrained decorrelation classification method with structure and label information. The proposed methods not only contain the structure and label information of training samples, but also have low computational cost owing to the use of $$l_2$$-norm. All three methods have closed-form solutions, rendering them easy to solve and calculate efficiently. Importantly, the proposed methods can achieve better recognition results than most existing state-of-the-art sparse representation methods. Furthermore, based on the proposed methods, we illustrate the effect of different regularization constraints on the recognition performance. Experiments on the ORL, Extended YaleB, FERET, and LFW databases validate the effectiveness of the proposed methods.

A Transform Domain Implementation of Sparse Representation Method for Robust Face Recognition

Recently, a new discriminative sparse representation method for robust face recognition via $$\textit{l}_2$$ regularization (NDSRFR) was reported. In this contribution, a transform domain (TDNDSRFR) is presented. The discrete cosine transform implementation of the TDNDSRFR is given and shown to maintain the recognition accuracy of the NDSRFR while yielding considerable reduction in the computational complexity and storage requirements. Also, a technique that selects the balance parameter is introduced. Extensive simulations were performed on six face databases, namely, ORL, YALE, FERET, FEI, Cropped AR, and Georgia Tech, and sample results are given which confirm the enhanced properties of the TDNDSRFR.

Sparsity augmented discriminative sparse representation for face recognition

Sparse representation-based classification (SRC) has acquired prominent capability in fields of machine learning and pattern recognition. Collaborative representation-based classification (CRC) has achieved a comparable recognition performance with higher speed compared to SRC, which has attracted much attention because it enables to forgo the computationally quite expensive l1-norm sparsity constraint. However, the traditional CRC method neglects the discriminability of representation and recent study has claimed that the sparsity should not be completely neglected for computational costs. In this paper, we propose a sparsity-augmented discriminative sparse representation-based classification method which considers the discriminability and sparsity of representation via augmenting an l2-norm regularization discriminative sparse representation with a computationally inexpensive sparse representation. We utilize an efficient classification method to achieve better performance with a comparable classification time. Experimental results on four face databases show the effectiveness of our proposed method.

Robust visual tracking via nonlocal regularized multi-view sparse representation

The multi-view sparse representation based visual tracking has attracted increasing attention because the sparse representations of different object features can complement with each other. Since the robustness of different object features is actually not the same in challenging video sequences, it may contain unreliable features (the features with low robustness) in multi-view sparse representation. In this case, how to highlight the useful information of unreliable features for proper multi-feature fusion has become a tough work. To solve this problem, we propose a multi-view discriminant sparse representation method for robust visual tracking, in which we firstly divide the multi-view observations into different groups, and then estimate the sparse representations of multi-view group projections for calculating the observation likelihood. The advantages of the proposed sparse representation method are two-folds: 1) It can properly fuse the observation groups with reliable and unreliable features by using an online updated discriminant matrix to explore the group similarity in multi-feature space. 2) It introduces a nonlocal regularizer to enforce the spatial smoothness among the sparse representations of different group projections, which can enhance the robustness of multi-view sparse representation. Experimental results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.

Video semantic analysis based kernel locality-sensitive discriminative sparse representation

Kernel based locality-sensitive sparse representation is currently an active hot research topic in artificial intelligence, pattern recognition, signal processing and multimedia applications. In this paper, a new kernel based approach; named Video Semantic Analysis based Kernel Locality-Sensitive Discriminative Sparse Representation (KLSDSR) is proposed. This is to improve video semantic analysis for military intelligence systems and video surveillance. The proposed algorithm is able to learn more discriminative sparse representation (SR) coefficients based on group sparsity for video semantic analysis by incorporating both sparsity and locality-sensitive in kernel feature space with mapping of the SR features into a high dimensional space. Furthermore, an optimal dictionary is however engendered to compute the SR of video features aimed at good preservation of the locality structure among video semantic features and an improvement on computational cost. The proposed method gave promising classification results, when compared with state-of-the-art comparative approaches based on experimental results on video semantic concepts, which significantly improves the discrimination of SR features and it outperformed the other baseline methods.

Discriminative kernel sparse representation vial2regularisation for face recognition

A kernel-based discriminative sparse representation method (KDSR) via l2-norm regularisation for robust face recognition is proposed. Sparse representation in the original sample space is usually a linear representation which does not consider the non-linear relationship of samples. To overcome this limitation, KDSR first maps the sample into high-dimensional feature space via kernel tricks and then performs the discriminative sparse representation scheme in high-dimensional feature space. KDSR can capture the non-linear relationship of samples and contains more discriminative information of samples, which makes it have good classification performance. In addition, KDSR has a closed-form solution, which makes it computationally efficient and easy to apply in practice. Experimental results on three benchmark databases demonstrate that KDSR can achieve better recognition performance than many state-of-the-art methods.

Detection of Concealed Information Using Multichannel Discriminative Dictionary and Spatial Filter Learning

P300-based concealed information test is a well-known approach for detection of deception. Previous studies only used the parietal channel, Pz, or at most three midline frontal, central, and parietal electrodes (Fz, Cz, and Pz); however, various observations indicate that the P300 component is distributed over a wide area on the scalp. In this paper, we propose a discriminative sparse representation model that effectively makes use of the multichannel concealed information test data. More specifically, a discriminative spatial filter is incorporated into the sparse model. Based on the proposed objective function, the elements of the model (dictionary, spatial filter and sparse code) are iteratively updated such that the discrimination power of the multichannel sparse model is improved. Furthermore, the effect of sparsity constraint on the spatial filter coefficients was investigated. In order to evaluate the performance of the multichannel model in distinguishing guilty subjects from innocent ones, 44 subjects went through a mock crime scenario, and subsequently their electroencephalography signals were recorded using eight electrodes. Experimental results show that the multichannel discriminative dictionary and spatial filter learning method was superior to other multichannel methods allowing classification of guilty and innocent participants with 95% accuracy. Moreover, the learned spatial filters are consistent with the physiological evidence regarding the distribution of P300 subcomponents over the scalp.

Structure-Aware Local Sparse Coding for Visual Tracking.

Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm, which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show the local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous state-of-the-art methods.