Collaborative Representation-based Classification Research Articles

Local and Nonlocal Context-Aware Elastic Net Representation-Based Classification for Hyperspectral Images

By representing a query sample as a linear combination of all labeled samples and then classifying it by evaluating which class leads to the minimal representation error, the representation-based classification methods have been successfully used for the classification of hyperspectral images (HSI). According to the usage of different norms, the sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) methods have been presented in two different paradigms. The SRC promotes the use of few labeled samples, while the CRC encourages the use of all labeled samples to collaboratively represent the query one from all classes. However, when the limited labeled samples of different classes are unbalance, the learnt representation is hardly to reflect the particular characteristics of each class. To overcome this problem, this paper presents a novel graph based context-aware elastic net (ELN) model for the HSI classification. Under a generalized ELN framework, the proposed model is able to take full advantages of SRC and CRC. Specifically, by evaluating the spectral and spatial self-similarity of local and nonlocal neighbors, an ELN-coding neighborhood graph is constructed with image patch distance. Owing to the exploitation of the spectral-spatial context, a centralized sparsity norm is integrated into the optimization model and it can promote the local and global consistence preserving. Finally, an efficient solver for the proposed model is developed by using the well-known alternating direction method of multiplier. Experiments on several real datasets validated that the proposed method can outperform state-of-the-art algorithms in terms of classification accuracy. Furthermore, even with the limited unbalanced labeled samples the proposed method is robust.

Improving sparsity of coefficients for robust sparse and collaborative representation-based image classification

Sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) have shown promising classification results. Both methods are distance-based classifiers, and they represent a test sample with coefficients solved by different sparsity regularizations. The reason why the representation coefficient vector can be sparse is that the test sample can be represented by only a small subset of the whole dictionary, which means that only a few entries of the coefficient vector have nonzero values. Previous studies show that sparsity of representation coefficients of SRC and CRC is significant for achieving good classification performance. However, the parameter of the algorithm is closely associated with the sparsity and it is very hard to obtain the optimal parameter value. In this paper, we propose two novel versions of SRC and CRC and implement four algorithms. The first version has two implementation named SQ_SRC and SQ_CRC, which use squared value of each entry of the representation coefficient vector to enhance sparsity of representation coefficients. SQ_SRC and SQ_CRC allow us to obtain more robust representation and classification performance of the test sample. The second version has another two implementation named SQF_SRC and SQF_CRC, which integrate original SRC and CRC with the SQ_SRC and SQ_CRC to perform classification. We conduct extensive experiments on a number of facial datasets, as well as a couple of non-facial datasets. The experimental results demonstrated that our methods output much more promising classification results than naive SRC and CRC in most scenarios.

Multi-size patch based collaborative representation for Palm Dorsa Vein Pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm

This paper proposes a novel method, Multi-Size patch based Collaborative Representation based Classification (CRC) strategy by Enhanced Ensemble Learning, for palm dorsa vein pattern (PDVP) based human recognition employing thermal imaging. This thermal PDVP imaging based human recognition methodology has been specifically employed to encounter the challenging crisis of intrusion posed by imposters. To address the Small Sample Size (SSS) problem, intrinsic to many biometric applications, each image is transformed into multiple patches, leading to an increase in the total number of samples. In a bid to make the classification strategy less sensitive to the choice of patch-size, the present paper proposes a new enhanced ensemble learning for the patch based CRC (PCRC) algorithm, where margin maximization is carried out using exponential squared loss minimization. This work also proposes how this loss minimization can be achieved by a stochastic optimization algorithm and solves this problem using artificial bee colony (ABC) algorithm. In this context, a new ABC variant, called modified interactive artificial bee colony (MI-ABC) algorithm, has also been proposed, which has been demonstrated to outperform the basic ABC and its several modern variants. The proposed methodology has been implemented on a well–structured real database, developed in our laboratory using real subjects, and the results obtained in implementation phase clearly demonstrate that our proposed method could outperform its several competitors and achieve substantially high classification rates, for the problem under consideration.

Adaptive joint block-weighted collaborative representation for facial expression recognition

Facial expression recognition (FER) plays a significant role in human-computer interactions. Recently, regularized linear representation-based classification has achieved satisfying results in FER. Considering that different blocks in a sample should contribute differently to the representation and classification, we propose an adaptive joint block-weighted collaborative representation-based classification (JBW_CRC) method to effectively exploit the similarity and distinctiveness of different blocks. In JBW_CRC, samples are divided into different blocks and each block of the query sample is represented as a feature vector. Each feature vector is coded on its related block dictionary, which considers the similarity among the feature vectors. Additionally, the distinctiveness of different feature vectors is obtained by weighting its distance to other features, which addresses the distinctiveness in the different feature vectors. The proposed method is verified from the aspect of training samples, time complexity, and Gaussian noise variances on benchmark databases and the extensive experiments show that the proposed method is very competitive with some similar pattern classification methods.

Statistical binary patterns and post-competitive representation for pattern recognition

During the last decade, sparse representations have been successfully applied to design high-performing classification algorithms such as the classical sparse representation based classification (SRC) algorithm. More recently, collaborative representation based classification (CRC) has emerged as a very powerful approach, especially for face recognition. CRC takes advantage of SRC through the notion of collaborative representation, relying on the observation that the collaborative property is more crucial for classification than the l1-norm sparsity constraint on coding coefficients used in SRC. This paper follows the same general philosophy of CRC and its main novelty is the application of a virtual collaborative projection (VCP) routine designed to train images of every class against the other classes to improve fidelity before the projection of the query image. We combine this routine with a method of local feature extraction based on high-order statistical moments to further improve the representation. We demonstrate using extensive experiments of face recognition and classification that our approach performs very competitively with respect to state-of-the-art classification methods. For instance, using the AR face dataset, our method reaches 100% of accuracy for dimensionality 300.

Robust Hyperspectral Image Classification by Multi-Layer Spatial-Spectral Sparse Representations

Sparse representation (SR)-driven classifiers have been widely adopted for hyperspectral image (HSI) classification, and many algorithms have been presented recently. However, most of the existing methods exploit the single layer hard assignment based on class-wise reconstruction errors on the subspace assumption; moreover, the single-layer SR is biased and less stable due to the high coherence of the training samples. In this paper, motivated by category sparsity, a novel multi-layer spatial-spectral sparse representation (mlSR) framework for HSI classification is proposed. The mlSR assignment framework effectively classifies the test samples based on the adaptive dictionary assembling in a multi-layer manner and intrinsic class-dependent distribution. In the proposed framework, three algorithms, multi-layer SR classification (mlSRC), multi-layer collaborative representation classification (mlCRC) and multi-layer elastic net representation-based classification (mlENRC) for HSI, are developed. All three algorithms can achieve a better SR for the test samples, which benefits HSI classification. Experiments are conducted on three real HSI image datasets. Compared with several state-of-the-art approaches, the increases of overall accuracy (OA), kappa and average accuracy (AA) on the Indian Pines image range from 3.02% to 17.13%, 0.034 to 0.178 and 1.51% to 11.56%, respectively. The improvements in OA, kappa and AA for the University of Pavia are from 1.4% to 21.93%, 0.016 to 0.251 and 0.12% to 22.49%, respectively. Furthermore, the OA, kappa and AA for the Salinas image can be improved from 2.35% to 6.91%, 0.026 to 0.074 and 0.88% to 5.19%, respectively. This demonstrates that the proposed mlSR framework can achieve comparable or better performance than the state-of-the-art classification methods.

Weighted average integration of sparse representation and collaborative representation for robust face recognition

Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification. As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition. The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10% improvement in recognition accuracy.

Multiplication fusion of sparse and collaborative representation for robust face recognition

Sparse representation based classification (SRC) and collaborative representation based classification (CRC) are two well-known methods in representation-based classification for face recognition. SRC emphasizes on the role of sparsity in coding, while CRC obtains an enhanced sparse representation with collaborative contributions from all classes. Previous studies show that sparsity of representation coefficients of SRC and CRC is significant for robust classification that may amplify the nonlocal contribution from other classes. However, the parameters are closely associated with the sparsity and it is very difficult to solve the optimal solutions. This paper proposed a novel representation-based image classification method that integrates SRC with CRC by a multiplication operation on the solutions to obtain enhanced sparse representation. We conducted experiments on several benchmark face databases and the experimental results showed that our algorithm produces higher accuracy on classification than both SRC and CRC.

Locality Preserving Collaborative Representation for Face Recognition

Face recognition has many applications in pattern recognition and computer vision, and many face recognition methods have been proposed. Among them, the recently proposed collaborative representation based face recognition has attracted the attention of researchers. Many variants and extensions of collaborative representation based classification (CRC) have been presented. However, most of CRC methods do not consider data locality, which is crucial for classification task. In this article, a novel collaborative representation based face recognition method, LP-CRC, is proposed, which balances data locality and collaborative representation. The proposed method incorporates a locality adaptor term into the robust collaborative representation based classification framework, leading to a novel unified objective function. The Augmented Lagrange Multiplier is used to optimize the objective function. Tests on standard benchmarks demonstrate that the proposed face recognition method is superior to existing methods and robust to noise and outliers.

Hyperspectral face recognition with log‐polar Fourier features and collaborative representation based voting classifiers

Hyperspectral imagery analysis has become a popular topic for improving face recognition accuracy. Nevertheless, it encounters difficulty in data acquisition, low signal-to-noise ratio, and high dimensionality. As a result, there exists a need to develop better algorithms in order to achieve higher classification rates. In this study, the authors propose a new method for hyperspectral face recognition with very competitive experimental results. Since there is a significant amount of noise in every spectral band, they reduce noise adaptively from each spectral band by using any image denoising method, e.g. block matching and 3D filtering. They then crop each face according to its eye coordinates so that translation invariance can be achieved. They conduct log-polar transform to each cropped face image and extract 2D Fourier spectrum from them. In this way, the extracted features are approximately invariant to translation, rotation, and scaling. They use the collaborative representation-based classifier with voting for hyperspectral face recognition. They perform some experiments to test the authors’ new method for hyperspectral face recognition with very promising results.

Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder

Deep learning, which represents data by a hierarchical network, has proven to be efficient in computer vision. To investigate the effect of deep features in hyperspectral image (HSI) classification, this paper focuses on how to extract and utilize deep features in HSI classification framework. First, in order to extract spectral–spatial information, an improved deep network, spatial updated deep auto-encoder (SDAE), is proposed. SDAE, which is an improved deep auto-encoder (DAE), considers sample similarity by adding a regularization term in the energy function, and updates features by integrating contextual information. Second, in order to deal with the small training set using deep features, a collaborative representation-based classification is applied. Moreover, in order to suppress salt-and-pepper noise and smooth the result, we compute the residual of collaborative representation of all samples as a residual matrix, which can be effectively used in a graph-cut-based spatial regularization. The proposed method inherits the advantages of deep learning and has solutions to add spatial information of HSI in the learning network. Using collaborative representation-based classification with deep features makes the proposed classifier extremely robust under a small training set. Extensive experiments demonstrate that the proposed method provides encouraging results compared with some related techniques.

Occluded Face Recognition Based on Block-Label and Residual

We consider the problem of automatically human face recognition from frontal views with occlusion and disguise. Since the occlusion can make the query image deviated from the normal distribution, most prior block-based methods focus on reducing the occlusion influence on the global-based representation. Our method is also block-based, but the blocks are non-uniform. Each block contains a certain face component such as eyes, cheek, and forehead, which makes the block more physically meaningful. Recently, sparse representation-based classification (SRC) and Collaborative representation-based classification (CRC) are applied to image recognition successfully, so we classify each block by SRC or CRC respectively. Then the occlusion pixels can be estimated by residuals. Besides, based on the distribution of block labels and the corresponding residual, we propose a new rule for the selection of block and the coefficients for the reconstruction. The final identification is performed on the un-occluded part of each image. Experiments on the AR, extended Yale B and CMU-PIE database verify the robustness and effectiveness of our method.

Blockwise collaborative representation‐based classification via L 2 ‐norm of query data for accurate face recognition

A new blockwise collaborative representation-based classification with L2-norm of test data for accurate face recognition is presented. For training we divide images into several blocks and estimate representation coefficients of each block via L2-norm minimisation. For testing, the L2-norm of test image blocks are scaled by the trained representation coefficients. A novel classification scheme based on the L2-norm of test blocks is proposed and this scheme is jointly applied with conventional reconstruction error-based classification. Experimental results show that the proposed methods outperform other representation-based methods for face recognition.

Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition.

Collaborative representation-based classification (CRC) and sparse RC (SRC) have recently achieved great success in face recognition (FR). Previous CRC and SRC are originally designed in the real setting for grayscale image-based FR. They separately represent the color channels of a query color image and ignore the structural correlation information among the color channels. To remedy this limitation, in this paper, we propose two novel RC methods for color FR, namely, quaternion CRC (QCRC) and quaternion SRC (QSRC) using quaternion ℓ1 minimization. By modeling each color image as a quaternionic signal, they naturally preserve the color structures of both query and gallery color images while uniformly coding the query channel images in a holistic manner. Despite the empirical success of CRC and SRC on FR, a few theoretical results are developed to guarantee their effectiveness. Another purpose of this paper is to establish the theoretical guarantee for QCRC and QSRC under mild conditions. Comparisons with competing methods on benchmark real-world databases consistently show the superiority of the proposed methods for both color FR and reconstruction.

Face recognition using class specific dictionary learning for sparse representation and collaborative representation

Recently, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been successfully used for visual recognition and have demonstrated impressive performance. Given a test sample, SRC or CRC formulates its linear representation with respect to the training samples and then computes the residual error for each class. SRC or CRC assumes that the training samples from each class contribute equally to the dictionary in the corresponding class, i.e., the dictionary consists of the training samples in that class. This, however, leads to high residual error and instability. To overcome this limitation, we propose a class specific dictionary learning algorithm. To be specific, by introducing the dual form of dictionary learning, an explicit relationship between the basis vectors and the original image features is represented, which also enhances the interpretability. SRC or CRC can be thus considered as a special case of the proposed algorithm. Blockwise coordinate descent algorithm and Lagrange multipliers are then adopted to optimize the corresponding objective function. Extensive experimental results on five benchmark face recognition datasets show that the proposed algorithm achieves superior performance compared with conventional classification algorithms.

Integrating absolute distances in collaborative representation for robust image classification

Conventional sparse representation based classification (SRC) represents a test sample with the coefficient solved by each training sample in all classes. As a special version and improvement to SRC, collaborative representation based classification (CRC) obtains representation with the contribution from all training samples and produces more promising results on facial image classification. In the solutions of representation coefficients, CRC considers original value of contributions from all samples. However, one prevalent practice in such kind of distance-based methods is to consider only absolute value of the distance rather than both positive and negative values. In this paper, we propose an novel method to improve collaborative representation based classification, which integrates an absolute distance vector into the residuals solved by collaborative representation. And we named it AbsCRC. The key step in AbsCRC method is to use factors a and b as weight to combine CRC residuals rescrc with absolute distance vector disabs and generate a new deviation r=a·rescrc−b·disabs, which is in turn used to perform classification. Because the two residuals have opposite effect in classification, the method uses a subtraction operation to perform fusion. We conducted extensive experiments to evaluate our method for image classification with different instantiations. The experimental results indicated that it produced a more promising result of classification on both facial and non-facial images than original CRC method.

Parity symmetrical collaborative representation-based classification for face recognition

Although the subspace-based feature extraction algorithms provided a feasible strategy to deal with the classification of high-dimensional data, most of the existing algorithms are locality-oriented and suffer from many difficulties such as uncertain information associated with dataset and small sample size problem. In this paper, we propose a novel collaborative representation-based classification method using parity symmetry strategy for face recognition. More specifically, we firstly synthesize a set of parity symmetrical images by means of odd–even decomposition theorem, aiming to augment the training set. Secondly, each query sample is represented as a linear combination of the training samples from the extended training set, we then exploit the optimal representation of each reconstructed image with relevant contribution from each class. The final goal of the proposed method is to generate the best parity symmetrical representation of the query sample to perform robust face classification. Experimental results conducted on ORL, FERET, AR, PIE and LFW face databases demonstrate the effectiveness of the proposed method.

Scene classification using local and global features with collaborative representation fusion

This paper presents an effective scene classification approach based on collaborative representation fusion of local and global spatial features. First, a visual word codebook is constructed by partitioning an image into dense regions, followed by the typical k-means clustering. A locality-constrained linear coding is employed on dense regions via the visual codebook, and a spatial pyramid matching strategy is then used to combine local features of the entire image. For global feature extraction, the method called multiscale completed local binary patterns (MS-CLBP) is applied to both the original gray scale image and its Gabor feature images. Finally, kernel collaborative representation-based classification (KCRC) is employed on the extracted local and global features, and class label of the testing image is assigned according to the minimal approximation residual after fusion. The proposed method is evaluated by using four commonly-used datasets including two remote sensing images datasets, an indoor and outdoor scenes dataset, and a sports action dataset. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods.

Dimension reduction using collaborative representation reconstruction based projections

This paper develops a collaborative representation reconstruction based projections (CRRP) method for dimension reduction. Collaborative representation based classification (CRC) is much faster than sparse representation based classification (SRC) while owning the similar recognition performance to SRC. Both CRC and SRC utilize the class reconstruction error for classification. First, CRRP characterizes the between-class/within-class reconstruction error using collaborative representation; Second, CRRP seeks the projections by maximizing the between-class reconstruction error to the within-class reconstruction error. So the proposed method is called CRRP. The experimental results on AR, Yale B and CMU PIE face databases demonstrate that CRRP is an effective dimension reduction method.

Optimized projection for Collaborative Representation based Classification and its applications to face recognition

Collaborative Representation based Classification (CRC) is powerful for face recognition and has lower computational complexity than Sparse Representation based Classification (SRC). To improve the performance of CRC, this paper proposes a new dimensionality reduction method called Optimized Projection for Collaborative Representation based Classification (OP-CRC), which has the direct connection to CRC. CRC uses the minimum reconstruction residual based on collaborative representation as the decision rule. OP-CRC is designed according to this rule. The criterion of OP-CRC is maximizing the collaborative representation based between-class scatter and minimizing the collaborative representation based within-class scatter in the transformed space simultaneously. This criterion is solved by iterative algorithm and the algorithm converges fast. CRC performs very well in the transformed space of OP-CRC. Experimental results on Yale, AR, FERET, CMU_PIE and LFW databases show the effectiveness of OP-CRC in face recognition.