Collaborative Representation Based Classification Method Research Articles

Margin embedding net for robust margin collaborative representation-based classification

Collaborative Representation-based Classification method (CRC) shows great potential in classification task. However, redundancies in both features and samples limit the application of CRC seriously. The existing works only solve one of them and ignore the other, which leads to performance degradation. To address this problem, we explore collaborative representation mechanism and propose a classification method termed Robust Margin Collaborative Representation-based Classification (RMCRC) which uses a few but more representative robust marginal samples to eliminate redundancy between samples. As the performance of RMCRC is related to robust marginal samples and class separability assumption closely, we further propose a feature extraction method termed Margin Embedding Net (MEN) for RMCRC. In MEN, virtual samples are generated by a generative model to enhance effectiveness of robust marginal samples and generalizability of RMCRC. Then, an embedding network with triplet loss is used to eliminate the redundancy in features and ensure the assumption is satisfied. Specifically, we construct triplet according to the collaborative representation. Hence, MEN fits RMCRC very well. Extensive experimental results validate effectiveness of proposed method.

A sparsity augmented probabilistic collaborative representation based classification method

In order to enhance the performance of image recognition, a sparsity augmented probabilistic collaborative representation based classification (SA-ProCRC) method is presented. The proposed method obtains the dense coefficient through ProCRC, then augments the dense coefficient with a sparse one, and the sparse coefficient is attained by the orthogonal matching pursuit (OMP) algorithm. In contrast to conventional methods which require explicit computation of the reconstruction residuals for each class, the proposed method employs the augmented coefficient and the label matrix of the training samples to classify the test sample. Experimental results indicate that the proposed method can achieve promising results for face and scene images. The source code of our proposed SA-ProCRC is accessible at https://github.com/yinhefeng/SAProCRC

Representation-based classification methods with enhanced linear reconstruction measures for face recognition

Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.

A new discriminative collaborative representation-based classification method via l2 regularizations

Collaborative representation-based classification (CRC) is one of the famous representation-based classification methods in pattern recognition. However, a testing sample in most of the CRC variants is collaboratively reconstructed by a linear combination of all the training samples from all the classes, the training samples from the class that the testing sample belongs to have no advantage in discriminatively and competitively representing and classifying the testing sample. Moreover, the incorrect classification can easily come into being when the training samples from the different classes are very similar. To address the issues, we propose a novel discriminative collaborative representation-based classification (DCRC) method via $$l_2$$ regularizations to enhance the power of pattern discrimination. In the proposed model, we consider not only the discriminative decorrelations among all the classes, but also the similarities between the reconstructed representation of all the classes and the class-specific reconstructed representations in the $$l_2$$ regularizations. The experiments on several public face databases have demonstrated that the proposed DCRC effectively and robustly outperforms the state-of-the-art representation-based classification methods.

A novel two-stage scene classification model based on feature variable significance in high-resolution remote sensing

In this article, we focus on the high-resolution remote sensing scene classification, which aims to label the scene image according to its content. While the high similarity of the between-class and variety of the inner class in the scene images make it challenging. To address the issue, we propose a unified two-stage classification framework named variable-weighted multi-feature fusing (VWMF) method. In the first stage, we fuse multiple low-level features into a concatenated holistic histogram feature via pre-trained unsupervised models. Then, we use a kernel collaborative representation-based classification method to predict the two most possible categories. In the second stage, for the two possible categories, we calculate the variable-weighted feature through a pre-calculated feature variable weight set of the corresponding two categories, and then put the variable-weighted feature into the pre-trained SVM for one-versus-one classification. Comprehensive experiments on two publicly available data sets demonstrate the superiority of our VWMF method compared with state-of-the-art remote sensing scene classification methods.

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.

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.

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.

Robust collaborative representation-based classification via regularization of truncated total least squares

Collaborative representation-based classification has shown promising results on cognitive vision tasks like face recognition. It solves a linear problem with $$l_1$$ or $$l_2$$ norm regularization to obtain a stable sparse representation. Previous studies showed that the collaboration representation assisted the output of optimum sparsity constraint, but the choice of regularization also played a crucial role in stable representation. In this paper, we proposed a novel discriminative collaborative representation-based classification method via regularization implemented by truncated total least squares algorithm. The key idea of the proposed method is combining two coefficients obtained by $$l_2$$ regularization and truncated TLS-based regularization. After evaluated by extensive experiments conducted on several benchmark facial databases, the proposed method is demonstrated to outperform the naive collaborative representation-based method, as well as some other state-of-the-art methods for face recognition. The regularization by truncation effectively and dramatically enhances sparsity constraint on coding coefficients in collaborative representation and increases robustness for face recognition.

Learn a Hybrid Collaborative Representation for Fine-Grained Image Classification

Image classification has attracted more and more interest over the recent years. Consequently, a number of excellent non-parametric classification algorithms, such as collaborative representation based classification (CRC), have emerged and achieved superior performance to parametric classification algorithms. However, for fine-grained image classification task, both the class specific attributes and the shared attributes play significant roles in describing the image. CRC scheme does not consider the characteristics and merely utilizes all attributes without separation to represent an image. In this paper, we propose a hybrid collaborative representation based classification method to describe an image from perspective of the shared features, as well as the class specific features. Moreover, to reduce the representation error and obtain precise description, we learn a dictionary for hybrid collaborative representation with the training samples. We conduct extensive experiments on fine-grained image datasets to verify the superior performance of our proposed algorithm compared with the conventional approaches.

A New Virtual Samples-Based CRC Method for Face Recognition

The research of automatic face recognition has attracted much attention from many researchers because of human faces’ uniqueness and usability. However, in the real-world applications, the acquisition equipment of face images is affected by illumination changes, facial expression variations, different postures and other environment factors, resulting in limited number of face images collected. This situation has become an obstacle to the development of face recognition technology. Therefore, in this paper, we utilize the information of the left-half face and right-half face to generate respectively two virtual ‘axis-symmetrical’ face images from an original face image and adopt collaborative representation based classification method (CRC) to perform classification. The first and second virtual face images convey more information of the right-half face and left-half face, respectively. Experiments have been performed on the Extended Yale_B, ORL, AR and FERET face databases and the experimental results show that our method can improve the recognition accuracy effectively.

The fusion of original and symmetric virtual images for image preprocessing in face recognition and collaborative representation based classification

Various poses, facial expressions and illuminations are the biggest challenges in the fields of face recognition. To overcome these challenges, we propose a simple yet novel method in this paper by using the approximately symmetrical virtual face. Firstly, based on the symmetrical characteristics of faces, we present the method to generate the virtual faces for all samples in detail. Secondly, the collaborative representation based classification method is performed on both of the original set and virtual set individually. In this way, two kinds of representation residuals of every class can be obtained. Thirdly, an adaptive weighted fusion approach is presented and utilized to integrate those two kinds of representation residuals for face recognition. Lastly, we can obtain the label of the test sample by assigning it to the class with the minimum fused residual. Several experiments are conducted which show that the proposed method not only can greatly improve the classification accuracy, but also can effectively reduce the negative influence of various poses, illuminations, and facial expressions.

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.

Virtual images inspired consolidate collaborative representation-based classification method for face recognition

The collaborative representation-based classification method performs well in the field of classification of high-dimensional images such as face recognition. It utilizes training samples from all classes to represent a test sample and assigns a class label to the test sample using the representation residuals. However, this method still suffers from the problem that limited number of training sample influences the classification accuracy when applied to image classification. In this paper, we propose a modified collaborative representation-based classification method (MCRC), which exploits novel virtual images and can obtain high classification accuracy. The procedure to produce virtual images is very simple but the use of them can bring surprising performance improvement. The virtual images can sufficiently denote the features of original face images in some case. Extensive experimental results doubtlessly demonstrate that the proposed method can effectively improve the classification accuracy. This is mainly attributed to the integration of the collaborative representation and the proposed feature-information dominated virtual images.

Using the original and symmetrical face training samples to perform collaborative representation for face recognition

More training samples are able to reveal more possible variation of the illumination, expression and poses and are consequently beneficial for correct classification. However, in real-world applications, there are usually only a limited number of available training samples. Therefore, it is hard to effectively improve the accuracy of face recognition. The symmetry of face is of great importance to face recognition. In this paper, based on the symmetry of the face, the new mirror training samples are first generate new samples. Then the original training samples and the generated symmetry training samples are, respectively, used to perform collaborative representation based classification method. Finally, the scheme of the score level fusion is adopted to integrate the original training samples and symmetrical face training samples for ultimate face recognition by assigning a larger weight to the original training samples. The experimental results show that the proposed method can classify the face with a high accuracy.