Representation-based Classification Method Research Articles

Sample partition and grouped sparse representation

Recently, conventional representation-based classification (RBC) methods demonstrate promising performance in image recognition. However, conventional RBCs only use a kind of deviations between the test sample and the linear combination of training samples of each class to perform classification. In many cases, a single kind of deviations corresponding to each class cannot effectively reflect the difference between the test sample and reconstructed sample of each class. Moreover, in practical applications, limited training samples are not able to reflect the possible changes of the image sufficiently. In this paper, we propose a novel scheme to tackle the above-mentioned problems. Specifically, we first use the original training samples to generate corresponding mirror samples. Thus, the original sample set and its mirror counterpart are treated as two separate training groups. Secondly, we perform collaborative representation classification on these two groups from which each class leads to two kinds of deviations, respectively. Finally, we fuse two kinds of deviations of each class and their correlation coefficient to classify the test sample. The correlation coefficient is defined for two kinds of deviations of each class. Experimental results on four databases show the proposed scheme can improve the recognition rate in image-based recognition.

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.

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.

Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure

The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structureRSRC-ASP) is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals aggregation. In addition, three strategies, local-neighbourhood representation local intra-class Bayesian residual criterion and local auxiliary dictionary are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems.

Sparse representation for robust face recognition by dictionary decomposition

Sparse representation-based classification (SRC) method has gained great success in face recognition due to its encouraging and impressive performance. However, in SRC the data used to train or test are usually corrupted, and hence the performance is affected. This paper proposes a robust face recognition approach by means of learning a class-specific dictionary and a projection matrix. Firstly, the training data are decomposed into class-specific dictionary, non-class-specific dictionary, and sparse error matrix. Secondly, in order to correct the corrupted test data, the data are projected onto their corresponding underlying subspace, and a projection matrix between the original training data and the class-specific dictionary is learned. Then, the features of the class-specific dictionary and the corrected test data are extracted by using Eigenface method. Finally, the SRC is performed to classify. Extensive experiments conducted on publicly available data sets show that the proposed algorithm performs better than some state-of-the-art methods.

Vehicle recognition in acoustic sensor networks using multiple kernel sparse representation over learned dictionaries

Sparse representation–based classification and kernel methods have emerged as important methods for pattern recognition. In this work, we study the problem of vehicle recognition using acoustic sensor networks in real-world applications. To improve the recognition accuracy with noise sensor data collected from challenging sensing environments, we develop a new method, called multiple kernel sparse representation–based classification, for vehicle recognition. In the proposed multiple kernel sparse representation–based classification method, acoustic features of vehicles are extracted and mapped into a high-dimensional feature space using a kernel function, which combines multiple kernels to obtain linearly separable samples. To improve the recognition accuracy, we incorporate dictionary learning method K-singular value decomposition into the multiple kernel sparse representation–based classification framework. The vehicle recognition from acoustic sensor network is then formulated into an optimization problem. Our extensive experimental results demonstrate that the proposed multiple kernel sparse representation–based classification method with learned dictionaries outperforms other existing methods in the literature on vehicle recognition from complex acoustic sensor network datasets.

Hierarchical sparse representation with deep dictionary for multi-modal classification

Sparse representation based classification (SRC) methods have achieved many successes in pattern recognition and machine learning. In such methods, the training samples of all categories are mixed and compose a dictionary to represent the test sample via sparsity constraint. Then, the class with the minimum representation error wins for labeling the test sample. In general, SRC is more flexible and effective than many supervised learning methods. However, in some cases it is unlikely to represent the test sample accurately, which tends to undermine the classification accuracy. To alleviate this issue, we propose a hierarchical sparse representation based classification method by augmenting the single-layer sparse representation into the hierarchical representation with a deep dictionary. Specifically, the features from all training samples are first divided into several groups according to their labels. Then we employ hierarchical clustering in each group and combine them to form a deep dictionary such that the root layer includes only a certain amount of the most representative exemplars while the subsequent layers focus on characterizing the remaining individual information across different groups. Furthermore, we use the layer-after-layer residuals to encode the variation patterns across individuals in different scales. Given the deep dictionary, a hierarchical sparse representation based classification method is presented to determine the label for each test sample by iteratively representing its primary part with the exemplars in different groups but the remaining parts by the variation patterns encoded in different layers. To further improve the classification accuracy and robustness, we extend our method by taking advantage of the complementary information in multi-view features. Experiments on Multiple Features Data Set show promising results compared with the state-of-the-art classification methods.

Hyperspectral Image Classification with Spatial Filtering and \(l_{(2,1)}\) Norm

Recently, the sparse representation based classification methods have received particular attention in the classification of hyperspectral imagery. However, current sparse representation based classification models have not considered all the test pixels simultaneously. In this paper, we propose a hyperspectral classification method with spatial filtering and norm (SFL) that can deal with all the test pixels simultaneously. The norm regularization is used to extract relevant training samples among the whole training data set with joint sparsity. In addition, the norm loss function is adopted to make it robust for samples that deviate significantly from the rest of the samples. Moreover, to take the spatial information into consideration, a spatial filtering step is implemented where all the training and testing samples are spatially averaged with its nearest neighbors. Furthermore, the non-negative constraint is added to the sparse representation matrix motivated by hyperspectral unmixing. Finally, the alternating direction method of multipliers is used to solve SFL. Experiments on real hyperspectral images demonstrate that the proposed SFL method can obtain better classification performance than some other popular classifiers.

A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition.

Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequency-temporal optimized feature sparse representation-based classification method. Optimal channels are selected based on relative entropy criteria. Significant CSP features on frequency-temporal domains are selected automatically to generate a column vector for sparse representation-based classification (SRC). We analyzed the performance of the new method on two public EEG datasets, namely BCI competition III dataset IVa which has five subjects and BCI competition IV dataset IIb which has nine subjects. Compared to the performance offered by the existing SRC method, the proposed method achieves average classification accuracy improvements of 21.568 and 14.38% for BCI competition III dataset IVa and BCI competition IV dataset IIb, respectively. Furthermore, our approach also shows better classification performance when compared to other competing methods for both datasets.

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.

Pose-based gait recognition with local gradient descriptors and hierarchically aggregated residuals

We focus on the problem of pose-based gait recognition. Our contribution is two-fold. First, we incorporate a local histogram descriptor that allows us to encode the trajectories of selected limbs via a one-dimensional version of histogram of oriented gradients features. In this way, a gait sequence is encoded into a sequence of local gradient descriptors. Second, we utilize a robust encoding method in which the residuals of local descriptors, with respect to a discriminative model, are aggregated into fixed length vectors. This technique combines the advantages of both residual aggregation and soft-assignment techniques, resulting in a powerful vector representation. For classification purposes, we use a nonlinear kernel to map vectors into a reproducing kernel Hilbert space. Then, we classify an encoded gait sequence according to the sparse representation-based classification method. Experimental evaluation on two publicly available datasets demonstrates the effectiveness of the proposed scheme on both recognition and verification tasks.

Partial Data Ear Recognition From One Sample per Person

The relatively stable structure of the human ear makes it suitable for identification. The significance of ear recognition in human authentication has become prominent in recent years. A number of ear recognition systems and methods have achieved good performance under limited conditions in the laboratory. In real-world applications, however, such as passport identification and law enforcement, where usually only one sample per person (OSPP) is registered in the gallery, most of the existing ear recognition methods are paralyzed by partial data (e.g., pose variations and occlusion). To address such problems, we propose a weighted multikeypoint descriptor sparse representation-based classification method to use local features of ear images. By adding adaptive weights to all the keypoints on a query image, the intraclass variations are reduced. Besides, the interclass variations of the gallery samples are enlarged by purifying the multikeypoint dictionary. Experiments are carried out on two benchmark databases, i.e., the Indian Institute of Technology Delhi ear database and the University of Science and Technology Beijing ear image database III, to demonstrate the feasibility and effectiveness of the proposed method in dealing with partial data problems in ear recognition under the premise of OSPP in the gallery. The proposed method has achieved state-of-the-art recognition performance especially when the ear images are affected by pose variations and random occlusion.

A supervised dimensionality reduction method-based sparse representation for face recognition

The sparse representation-based classification (SRC) method is a powerful tool to present high-dimensionality data and its superiority in many fields, especially in face recognition application has been proved. With sparsity appropriately harnessed, the SRC can solve face classification problems caused by varying expression, illumination as well as occlusion and disguise. However, face images as high-dimensionality data are usually noisy and the dimensionality is always larger than the number of training sample in real-world applications, which bring a disadvantage for the performance of SRC. Therefore, it is beneficial to perform dimensionality reduction (DR) before utilizing the SRC method. But most prevalent DR methods have no direct connection to SRC. In this paper, we proposed a supervised DR algorithm which suits SRC well and improves the discriminating ability in the low-dimensionality space. The proposed method utilizes the fisher discriminant criterion and low-dimensionality reconstructive restriction to extract the discriminating structure of data. The extensive experiments on public face databases verified the effectiveness of the supervised DR with the model of sparse representation.

Source cell phone verification from speech recordings using sparse representation

Source recording device recognition is an important emerging research field in digital media forensics. The literature has mainly focused on the source recording device identification problem, whereas few studies have focused on the source recording device verification problem. Sparse representation based classification methods have shown promise for many applications. This paper proposes a source cell phone verification scheme based on sparse representation. It can be further divided into three schemes which utilize exemplar dictionary, unsupervised learned dictionary and supervised learned dictionary respectively. Specifically, the discriminative dictionary learned by supervised learning algorithm, which considers the representational and discriminative power simultaneously compared to the unsupervised learning algorithm, is utilized to further improve the performances of verification systems based on sparse representation. Gaussian supervectors (GSVs) based on MFCCs, which have shown to be effective in capturing the intrinsic characteristics of recording devices, are utilized for constructing and learning dictionary. SCUTPHONE, which is a corpus of speech recordings from 15 cell phones, is presented. Evaluation experiments are conducted on three corpora of speech recordings from cell phones and demonstrate the effectiveness of the proposed methods for cell phone verification. In addition, the influences of number of target examples in the exemplar dictionary and size of the unsupervised learned dictionary on source cell phone verification performance are also analyzed.

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.

Hyperspectral Image Classification by Fusing Collaborative and Sparse Representations

This paper proposes to combine collaborative representation (CR) and sparse representation (SR) for hyperspectral image classification. SR may select too few samples that cannot well reflect within-class variations, while CR generates nonsparse code using all the atoms that may unfortunately include between-class interference. To alleviate these problems, two methods fusing CR and SR are proposed, i.e., a fused representation-based classification (FRC) method and an elastic net representation-based classification (ENRC) method. FRC attempts to achieve the balance between CR and SR in the residual domain, while ENRC uses a convex combination of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> penalties. Experimental results on two hyperspectral data demonstrate that the proposed methods outperform the original counterparts, i.e., CR-based classification (CRC) and SR-based classification (SRC).

Supervised Filter Learning for Representation Based Face Recognition.

Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances) in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP) features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment.

At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.

Hyperspectral Image Classification With Robust Sparse Representation

Recently, the sparse representation-based classification (SRC) methods have been successfully used for the classification of hyperspectral imagery, which relies on the underlying assumption that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples among the whole training dictionary. However, the SRC-based methods ignore the sparse representation residuals (i.e., outliers), which may make the SRC not robust for outliers in practice. To overcome this problem, we propose a robust SRC (RSRC) method which can handle outliers. Moreover, we extend the RSRC to the joint robust sparsity model named JRSRC, where pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few training samples and outliers. The JRSRC can also deal with outliers in hyperspectral classification. Experiments on real hyperspectral images demonstrate that the proposed RSC and JRSRC have better performances than the orthogonal matching pursuit (OMP) and simultaneous OMP, respectively. Moreover, the JRSRC outperforms some other popular classifiers.

Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers.

For patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimer's disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).