Collaborative Representation-based Classification Research Articles

Two-phase probabilistic collaborative representation-based classification

The probabilistic collaborative representation-based classification (PCRC), as a novel extension of collaborative representation-based classification (CRC), is a promising method in pattern recognition. In this article, we adopt the coarse to fine representation to propose two-phase probabilistic collaborative representation based-classification (TPCRC) to enhance the power of pattern discrimination in PCRC. In TPCRC, the first phase is to utilize probabilistic collaborative representation to coarsely choose the nearest representative samples, and the second phase is to use the chosen nearest samples to finely represent and classify each testing sample. In order to employ the locality of data to further improve classification performance of PCRC, we also propose two-phase weighted probabilistic collaborative representation based-classification (TWPCRC). In the fine representation of TWPCRC, the probabilistic collaborative coefficients are weighted by the local distance similarities between each testing sample and all the training samples. The proposed methods are verified by the comparative experiments on three public image data sets. Experimental results show that the proposed methods outperform the state-of-the-art collaborative representation-based classification methods.

Illumination invariant face recognition using dual-tree complex wavelet transform in logarithm domain

Abstract In this article, we develop a new algorithm for illumination invariant face recognition. We first transform the face images to the logarithm domain, which makes the dark regions brighter. We then use dual-tree complex wavelet transform to generate face images that are approximately invariant to illumination changes and use collaborative representation-based classifier to classify the unknown faces to one known class. We set the approximation sub-band and the highest two DTCWT coefficient sub-bands to zero values before the inverse DTCWT transform is performed. Experimental results demonstrate that our proposed method improves upon a few existing methods under both the noise-free and noisy environments for the Extended Yale Face Database B and the CMU-PIE face database.

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.

Joint deep convolutional feature representation for hyperspectral palmprint recognition

With discriminative information from various spectrums, hyperspectral imaging analysis has recently attracted more and more considerable research attention. This increase can also be attributed to an improvement in computer hardware that has led to the development of Convolutional Neural Networks (CNNs) achieving very high performances in numerous applications. Motivated by these technologies, a Joint Deep Convolutional Feature Representation (JDCFR) methodology is proposed for hyperspectral palmprint recognition. For a hyperspectral palmprint image cube, a CNN stack is constructed to extract its features from the entire spectral bands and generate a joint convolutional feature. The CNN stack contains dozens of CNNs with different parameter settings, which can be trained locally using palmprint images in different spectrums. To obtain a complete and nonredundant set of features and to avoid losing hierarchical characteristics hidden in different bands, the Joint Deep Convolutional Feature is represented by a Collaborative Representation-based Classifier (CRC) simultaneously to perform classification. Experimental results were conducted on a hyperspectral palmprint dataset consisting of 53 spectral bands with 110,770 images. Compared with other classifiers, CNNs, traditional palmprint recognition methods, as well as applying PCA to the feature matrix, the proposed method achieved the highest performance with 0.01% – EER and 99.62% – ARR.

Weighted Spatial Pyramid Matching Collaborative Representation for Remote-Sensing-Image Scene Classification

At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, the spatial pyramid matching (SPM) scheme, which considers spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we first introduce the spatial pyramid matching scheme to remote-sensing (RS)-image scene-classification tasks to improve performance. Then, we propose a weighted spatial pyramid matching collaborative-representation-based classification method, combining the CRC method with the weighted spatial pyramid matching scheme. The proposed method is capable of learning the weights of different subregions in representing an image. Finally, extensive experiments on several benchmark remote-sensing-image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm when compared with state-of-the-art approaches.

Histogram-Based CRC for 3D-Aided Pose-Invariant Face Recognition.

Traditional Collaborative Representation-based Classification algorithms for face recognition (CRC) usually suffer from data uncertainty, especially if it includes various poses and illuminations. To address this issue, in this paper, we design a new CRC method using histogram statistical measurement (H-CRC) combined with a 3D morphable model (3DMM) for pose-invariant face classification. First, we fit a 3DMM to raw images in the dictionary to reconstruct the 3D shapes and textures. The fitting results are used to render numerous virtual samples of 2D images that are frontalized from arbitrary poses. In contrast to other distance-based evaluation algorithms for collaborative (or sparse) representation-based methods, the histogram information of all the generated 2D face images is subsequently exploited. Second, we use a histogram-based metric learning to evaluate the most similar neighbours of the test sample, which aims to obtain ideal result for pose-invariant face recognition using the designed histogram-based 3DMM model and online pruning strategy, forming a unified 3D-aided CRC framework. The proposed method achieves desirable classification results that are conducted on a set of well-known face databases, including ORL, Georgia Tech, FERET, FRGC, PIE and LFW.

Jointly discriminative projection and dictionary learning for domain adaptive collaborative representation-based classification

In recent years, collaborative representation-based classification (CRC) methods have shown impressive performance in many recognition tasks. However, when the training data have different distributions with the testing data, the performance of CRC will be degraded significantly. On the other hand, concatenating training data from different sources as a single data set will affect the performance of CRC, as the shift exists between the different source domains. To address these problems, in this paper, we propose a Jointly Discriminative projection and Dictionary learning for domain adaptive Collaborative Representation-based Classification method (JD2-CRC). As the distributions of different source domains may be dissimilar, the data from all domains are projected into a common feature subspace where the latent shared structures can be found. Then a compact dictionary is learned to represent the projected data well. To find the most suitable projection matrices and dictionary for CRC, we design the objective function of JD2-CRC,according to the classification rule of CRC in feature subspace, which minimizes the ratio of within-class reconstruction errors over between-class reconstruction errors. Different to traditional optimization methods, an effective optimization procedure is presented based on gradient descent. Thus, the obtained collaborative representations have a better discriminability and suit the classification rule of CRC well. The experimental results demonstrate that the proposed method can achieve superior performance against other state-of-the-art methods.

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l2‐Norm Regularizations

Recently, collaborative representation‐based classification (CRC) and its many variations have been widely applied for various classification tasks in pattern recognition. To further enhance the pattern discrimination of CRC, in this article we propose a novel extension of CRC, entitled discriminative, competitive, and collaborative representation‐based classification (DCCRC). In the proposed DCCRC, the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation. The class discrimination information is well considered in the newly designed discriminative l2‐norm regularization that can decrease the ability of representation from the interclasses of each testing sample. Simultaneously, a competitive l2‐norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample. The effectiveness of the proposed DCCRC is explored by extensive experiments on the several public face databases and some real numerical UCI data sets. The experimental results demonstrate that the proposed DCCRC achieves the superior performance over the state‐of‐the‐art representation‐based classification methods.

Sparse Representation and Collaborative Representation? Both Help Image Classification

Image classification has attracted more and more attention. During the past decades, image classification has shown growing interest in representation-based classification methods, such as sparse representation-based classification and collaborative representation-based classification. However, the available representation-based methods still suffer from some problems. Especially, most methods only consider the shared representation of a test image. In this paper, we propose an elastic-net regularized regression algorithm (ENRR) for image classification. Specifically, our proposed method combines shared sparse representation with class specific collaborative representation when representing the test sample. Moreover, we extend the proposed ENRR to arbitrary kernel space to achieve better classification performance due to specificities and complexities of original images. The extensive experiments on face recognition datasets, handwritten recognition datasets, and remote sensing image datasets clearly demonstrate that the proposed ENRR outperforms several conventional methods in classification accuracy.

Domain adaptive collaborative representation based classification

Conventional representation based classification methods, such as sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been developed and shown great potential due to its effectiveness in various recognition tasks. However, when the test data and training data come from different distribution, the performance of SRC and CRC will be degraded significantly. Recently, several sparse representation based domain adaptation learning (DAL) methods have been proposed and achieve impressive performance. However, these sparse representation based DAL methods need to solve the l1-norm optimization problem, which is extremely time-consuming. To address this problem, in this paper, we propose a simple yet much more efficient domain adaptive collaborative representation-based classification method (DACRC). By replacing the l2-norm regularization term using the l2-norm, we exploit the collaborative representation rather than sparse representation to jointly learn projections of data in the two domains. In addition, a common dictionary is also learned such that in the projected space the learned dictionary can optimal represent both training and test data. Furthermore, the proposed method is effective to deal with multiple domains problem and is easy to kernelized. Compared with other sparse representation based DAL methods, DACRC is computationally efficient and its performance is better or comparable to many state-of-the-art methods.

Kernel cross-view collaborative representation based classification for person re-identification

Person re-identification aims at the maintenance of a global identity as a person moves among non-overlapping surveillance cameras. It is a hard task due to different illumination conditions, viewpoints and the small number of annotated individuals from each pair of cameras (small-sample-size problem). Common subspace learning methods have been proposed to handle the camera transition problems. However, after learning the low-dimensional representation, these methods usually compute distances using a simple cosine or Mahalanobis distance. Therefore, an still open question is how to better match probe and gallery images in the learned common subspace considering reduced number of training samples and the nonlinear behavior of the data. Collaborative Representation based Classification (CRC) has been employed successfully to address the small-sample-size problem in computer vision. However, the original CRC formulation is not well-suited for person re-identification since it does not consider that probe and gallery samples are from different cameras. Furthermore, it is a linear model, while appearance changes caused by different camera conditions indicate a strong nonlinear transition between cameras. To overcome such limitations, we propose the Kernel Cross-View Collaborative Representation based Classification (Kernel X-CRC), method that represents probe and gallery images by balancing representativeness and similarity nonlinearly. According to experimental results, we achieve state-of-the-art for rank-1 matching rates in three person re-identification datasets (CUHK03, PRID450S and GRID) and the second best results on VIPeR and CUHK01 datasets. Furthermore, we present outperforming results on Market-1501 dataset demonstrating that the Kernel X-CRC is suitable to a large-scale and multiple cameras scenario.

Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.

Prior Knowledge-Based Probabilistic Collaborative Representation for Visual Recognition.

Collaborative representation is an effective way to design classifiers for many practical applications. In this paper, we propose a novel classifier, called the prior knowledge-based probabilistic collaborative representation-based classifier (PKPCRC), for visual recognition. Compared with existing classifiers which use the collaborative representation strategy, the proposed PKPCRC further includes characteristics of training samples of each class as prior knowledge. Four types of prior knowledge are developed from the perspectives of image distance and representation capacity. They adaptively accommodate the contribution of each class and result in an accurate representation to classify a query sample. Experiments and comparisons on four challenging databases demonstrate that PKPCRC outperforms several state-of-the-art classifiers.

Collaborative representation with k-nearest classes for classification

Sparse representation based classification (SRC) and collaborative representation based classification (CRC), have been widely used in many fields such as face recognition and hyperspectral image classification. All known pattern classes of the training samples are used by SRC and CRC to collaborative represent a test sample, which may be time-consuming and unnecessary. This paper proposes the use of only a part of known pattern classes to collaboratively represent a test sample. Specifically, only the first k (a pre-given constant) classes that are nearest to the sample to be tested are used. With these k-nearest classes (KNCs), SRC and CRC, each can be generalized as KNCs based SRC (KNCSRC) and KNCs based CRC(KNCCRC), and conversely, SRC and CRC can be seen as special cases of KNCSRC and KNCCRC, respectively. Weighted SRC (WSRC) and weighted CRC (WCRC) also can be generalized as KNCs based WSRC (KNCWSRC) and KNCs based WCRC (KNCWSRC), respectively. Competitive classification experiments are carried out on five databases to evaluate the proposed classification scheme. The experimental results show that, in all cases, the proposed KNCSRC and KNCCRC outperform SRC and CRC, respectively.The results also prove that the proposed KNCWCRC outperforms WCRC. In conclusion, collaborative representation with KNCs can further improve collaborative representation based classification.

Collaborative representation based face classification exploiting block weighted LBP and analysis dictionary learning

Traditional collaborative representation based classification (CRC) method usually faces the challenge of data uncertainty hence results in poor performance, especially in the presence of appearance variations in pose, expression and illumination. To overcome this issue, this paper presents a CRC-based face classification method by jointly using block weighted LBP and analysis dictionary learning. To this end, we first design a block weighted LBP histogram algorithm to form a set of local histogram-based feature vectors instead of using raw images. By this means we are able to effectively decrease data redundancy and uncertainty derived from image noises and appearance variations. Second, we adopt an analysis dictionary learning model as the projection transform to construct an analysis subspace, in which a new sample is characterized with the improved sparsity of its reconstruction coefficient vector. The crucial role of the analysis dictionary learning method in CRC is revealed by its capacity of the collaborative representation in an analytic coefficient space. Extensive experimental results conducted on a set of well-known face databases demonstrate the merits of the proposed method.

Several robust extensions of collaborative representation for image classification

Collaborative representation (CR) is one of the well-known representation methods and has been widely used in computer vision and pattern recognition. The collaborative representation-based classification (CRC) and its extension called the probabilistic collaborative representation-based classification (PCRC) have obtained promising performance in image classification. However, the representation fidelity is usually measured by the ℓ2-norm , which is not robust to outliers. Moreover, CRC and PCRC only consider the global distribution of data and ignore the locality of data. To overcome those problems, in this paper, we propose the residual-based extensions and the weighted version of CRC and PCRC. Specifically, for the residual-based extensions of CRC and PCRC, we measure the representation fidelity by jointing ℓ1-norm and the ℓ2-norm of coding residuals. For the weighted extensions of CRC and PCRC with different coding residuals, we constrained the representation coefficients with locality of data. To verify the effectiveness of the proposed methods, we conduct extensive experiments on six popular face databases and three image databases. Experimental results have demonstrated that the ℓ1-norm of coding residual, jointing both the ℓ1-norm and the ℓ2-norm of coding residuals and the locality constraint of representation coefficients can enhance pattern discrimination effectively.

Dictionary Integration Using 3D Morphable Face Models for Pose-Invariant Collaborative-Representation-Based Classification

The paper presents a dictionary integration algorithm using 3D morphable face models (3DMM) for pose-invariant collaborative-representation-based face classification. To this end, we first fit a 3DMM to the 2D face images of a dictionary to reconstruct the 3D shape and texture of each image. The 3D faces are used to render a number of virtual 2D face images with arbitrary pose variations to augment the training data, by merging the original and rendered virtual samples to create an extended dictionary. Second, to reduce the information redundancy of the extended dictionary and improve the sparsity of reconstruction coefficient vectors using collaborative-representation-based classification (CRC), we exploit an on-line class elimination scheme to optimise the extended dictionary by identifying the training samples of the most representative classes for a given query. The final goal is to perform pose-invariant face classification using the proposed dictionary integration method and the on-line pruning strategy under the CRC framework. Experimental results obtained for a set of well-known face data sets demonstrate the merits of the proposed method, especially its robustness to pose variations.

Using the Original and Symmetrical Face Test Samples to Perform Two-Step Collaborative Representation for Face Recognition

Face recognition using sparse representation-based classification (SRC) is a new hot technique in recent years. However, the research indicates that it is the collaborative representation but not the [Formula: see text]-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a simple yet much more efficient face classification scheme, namely two-step collaborative representation-based classification (TSCRC) method. First, we exploit the symmetry of the face to generate new images of each test sample. Then, the original and new generated test samples are, respectively, used to perform TSCRC, which ultimately uses a small number of classes that are near to the test sample to represent and classify it. Finally, the score level fusion is taken to perform classification recognition. The experimental results clearly show that the proposed method has very competitive classification results.

Multiple kernel locality-constrained collaborative representation-based discriminant projection for face recognition

Abstract Collaborative representation-based classifier (CRC) has achieved superior classification performance in the field of face recognition. However, the performance of CRC will degrade significantly when facing non-linear structural data. To address this problem, many kernel CRC (KCRC) methods have been proposed. These methods usually use a predetermined kernel function which is difficult to be selected. In addition, how to select appropriate parameters remains a challenging problem. Hence, multiple kernel technology (MKL) is applied on CRC, which called MK-CRC. However, it only considers the representation errors while ignoring the class label information in the training process. In this paper, we propose a multiple kernel locality-constrained collaborative representation-based classifier (MKLCRC) which is the multiple kernel extension of CRC and considers the local structures of data. Based on the classification rule of MKLCRC, we propose a dimensionality reduction (DR) method called multiple kernel locality-constrained collaborative representation-based discriminant projection (MKLCR-DP). The goal of MKLCR-DP is to learn a projection matrix and a set of kernel weights to generate a low-dimensional subspace where the between-class reconstruction errors are maximized and the within-class reconstruction errors are minimized. Thus MKLCRC can achieve better performance in this low-dimensional subspace. The proposed method can be efficiently optimized with the trace ratio optimization. Experiments on AR, extended Yale B, FERET, CMU PIE and LFW face databases demonstrate that our method outperforms related state-of-the-art algorithms.

Collaborative representation based classifier with partial least squares regression for the classification of spectral data

The need for effective methods to classify high-dimensional spectral data is increasing in tasks such as rapid and non-destructive detection of object features and chemical species using spectroscopy. Partial least squares discriminant analysis (PLS-DA) is an effective, multivariate regression based method for spectral data classification. Although powerful, PLS-DA suffers from performance degradation under complex conditions such as nonlinearity, class imbalance and multiclass, which are common in real-world applications. Collaborative representation-based classifier (CRC) is a new machine learning algorithm which represents a query by a linear combination of training samples and classifies the query based on the representation. It offers the possibility of good classification performance even under nonlinearity, class imbalance and multiclass conditions. In this paper, we present a novel method for spectral data classification, namely CRC-WPLS, which reaps the benefits of both PLS regression and CRC. This method searches for a weighted, linear combination of all training samples to represent the query by using PLS regression, and then assigns the query to the class which yields the least approximation error. CRC-WPLS is compared to PLS-DA, kernel PLS-DA, support vector machine (SVM), random forest (RF) and representation-based classifiers on fourteen general machine learning datasets and three spectral datasets. Experimental results show the proposed method can outperform 7 baseline methods in most cases, and achieve a high classification accuracy (>90%) for low grade spectra obtained from portable instrumentation.