Sparse Representation-based Classification Algorithm Research Articles

Adaptive Classification Using Incremental Linearized Kernel Embedding

This paper considers the problem of adaptive classification for performing pattern discrimination in varying conditions when new data arrives. A new efficient method is presented to incrementally update features in a Nyström-approximated linearized kernel embedding (LKE). Our method leverages a fast eigendecomposition algorithm for symmetric Arrowhead matrices. The proposed method can also be applied to kernel principal component analysis (KPCA) or similar problems. A mechanism is proposed which allows the incremental linearized kernel embedding to be used for updating of dictionaries in a sparse representation-based classification algorithm. The method is based on transporting the dictionaries into the embedding expanded with new data points and avoids the need to learn new dictionary matrices every time new data becomes available. The effectiveness of the developed methods is illustrated on two handwritten digit image data sets namely MNIST and USPS. Classification performance before and after sequential embedding updates is evaluated and compared. Comparisons are also made between our incremental LKE algorithm and the conventional approach to updating the empirical kernel map in terms of their computational requirements and numerical stability.

Joint discriminative and shared dictionary learning with dictionary extension strategy for bearing fault classification

Sparse representation is one of the effective approaches for bearing fault diagnosis. Conventional sparse representation only focuses on fault feature extraction from bearing vibration signals while requiring experts to empirically make a diagnosis. This paper investigates bearing intelligent fault classification based on sparse representation based classification (SRC) method. A novel SRC algorithm, named joint discriminative and shared dictionary learning (JDSDL), is proposed for simultaneously characterize the class-specific and common features in vibration signals of bearings with different faults. The JDSDL seamlessly integrates discriminative dictionary learning, shared dictionary learning, and classifier training into a joint model to automatically recognize bearing faults. Moreover, a dictionary extension strategy is embedded within the proposed JDSDL model to make learned discriminative dictionary shift-invariant to improve its classification performance. The main advantages of the proposed method are that the JDSDL not only works well even if the training set is small, but also is robust to corruption of environment noises. Experimental results on three bearing datasets and comparison with competing methods are presented to demonstrate the effectiveness and superiority of the proposed JDSDL in bearing intelligent fault classification.

Mapping cotton cultivated area combining remote sensing with a fused representation-based classification algorithm

Cotton is one of the major cash crops in the world, which is the main fiber crop that delivers fibers to textile industries across the globe. The efficient and timely information of the cotton distribution is essential for cotton production, agricultural management, as well as the sustainability of agricultural systems. However, there still keep challenges in accurately extracting cotton cultivated area over large areas. To address this issue, we explored the feasibility of combining time series enhanced vegetation index (EVI) calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data with the fused representation-based classification (FRC) algorithm to identify the cotton pixels and map cotton cultivated area in the major cotton production regions of China. The Fourier transform was used to obtain the harmonic features of the annual time series EVI at a pixel scale over the study area. Then the FRC algorithm was applied for the identification of cotton cultivated area with harmonic features as input. By calculating the fused residual for each pixel, and setting the threshold value for each province based on the statistical data, the cotton cultivated area was extracted in the study area during 2015–2017. Finally, the performance of the FRC algorithm for cotton mapping was evaluated and compared with those of the collaborative representation-based classification (CRC) and sparse representation-based classification (SRC) algorithms. The results showed that there was a good correlation between the MODIS-derived cotton cultivated area and statistical data at the municipal level, with the coefficient of determination (R2) of 0.83 for 3 years. The mapping results of the cotton cultivated area derived by the FRC algorithm had a higher precision compared with those derived by the CRC and SRC algorithms. It demonstrated that the combination of time series MODIS EVI data, statistical data and the FRC algorithm is effective in identifying the cotton cultivated area. Furthermore, the applicability of this approach to other crop mapping and higher spatial resolution images is worth investigating.

Video action recognition with visual privacy protection based on compressed sensing

Action recognition in video involves interpreting complex semantic information, which remains a challenge in computer vision. Research on the use of deep neural networks for video action recognition has made significant progress in recent years, but most available recognition frameworks cannot satisfactorily perform the video action recognition task in privacy scene. In light of this issue, this paper proposes a method for video action recognition that employs a compressed sensing-based visual privacy protection framework. The proposed method protects visual privacy by balancing operational efficiency with the accuracy of recognition. It consists of three steps. First, compressed sensing is used to realize visual privacy protection and significantly enhance efficiency at the same time. Second, the convolutional 3D network model is used due to its speed to represent features of video actions in the compressed sensing data, and PCA is used to reduce the dimensionality of the extracted feature vectors to reduce temporal complexity. Finally, a sparse representation-based classification algorithm is integrated into the AdaBoost architecture to improve the recognition performance of the proposed method. Experiments were performed on three commonly used video action datasets to verify the proposed method, and the results show that it is robust in video action recognition tasks, and adequately protects visual privacy.

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

Neighborhood preserving sparse representation based on Nyström method for image set classification on symmetric positive definite matrices

In the field of pattern recognition, using the symmetric positive-definite matrices to represent image set has been widely studied, and sparse representation-based classification algorithm on the symmetric positive-definite matrix manifold has attracted great attention in recent years. However, the existing kernel representation-based classification methods usually use kernel trick with implicit kernel to rewrite the optimization function and will have some problems. To address the problem, a neighborhood preserving explicit kernel representation-based classification-based Nyström method is proposed on symmetric positive-definite manifold by embedding the symmetric positive-definite matrices into a Reproducing Kernel Hilbert Space with an explicit kernel based on Nyström method. Thus, we can take full advantage of kernel space characteristics. Through the experimental results, we demonstrate the better performance of our method in the task of image set classification.

Class-specific adaptive sparse representation via reweighted ℓ 1 norm minimization for image classification

The objective function of sparse representation to be minimized mainly consists of two parts: reconstruction error and sparsity-inducing regularization (e.g., l 0 norm or l 1 norm). A regularization parameter allows a trade-off between the two parts. In practical applications, solutions for sparse representation models are highly sensitive to the regularization parameter. To alleviate this phenomenon and improve the discrimination of sparse representation, a reweighted l 1 norm minimization-based sparse representation, named class-specific adaptive sparse representation (CSASR), is proposed. In CSASR, a class-specific weight matrix derived from observations of a class is applied to substitute the regularization parameter. The CSASR model mainly has the following two advantages: (i) the class-specific weight matrix can be calculated adaptively from observations; therefore, it is a parameter-free model and (ii) each class has a unique class-specific weight matrix, which enhances the discrimination of sparse representation. The experimental results show that the proposed algorithm achieves superior classification performance compared to traditional sparse representation-based classification algorithms.

Sparse Representation-Based Open Set Recognition.

We propose a generalized Sparse Representation-based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms.

A dimensionality reduction method based on structured sparse representation for face recognition

Face recognition (FR) has been one of the most fundamental problems in computer vision. Two issues are always concerned in a FR task: one is the dimensionality reduction (DR) of the features, and the other is the sparse representation for the samples. DR is an important step because it can not only reduce the storage space of face images, but also enhance the discrimination of the features. Meanwhile, sparse representation based classification (SRC) has been proved a powerful method to solve the problem of dimensionality. It simply considers the training samples as the dictionary to represent the testing samples. However, most of the SRC algorithms do not consider the structure of the dictionary. To consider these two aspects, in this paper, we proposed a FR method by combining a new DR model with the structured sparse representation (SSR). The key idea is projecting the images on a learned projection matrix, and performing the face classification by the SSR considering the structure information of the dictionary. The validity of the proposed method is verified by the evaluations on three databases.

Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition

This paper proposes a progressive sparse representation-based classification algorithm using local discrete cosine transform (DCT) evaluation to perform face recognition. Specifically, the sum of the contributions of all training samples of each subject is first taken as the contribution of this subject, then the redundant subject with the smallest contribution to the test sample is iteratively eliminated. Second, the progressive method aims at representing the test sample as a linear combination of all the remaining training samples, by which the representation capability of each training sample is exploited to determine the optimal “nearest neighbors” for the test sample. Third, the transformed DCT evaluation is constructed to measure the similarity between the test sample and each local training sample using cosine distance metrics in the DCT domain. The final goal of the proposed method is to determine an optimal weighted sum of nearest neighbors that are obtained under the local correlative degree evaluation, which is approximately equal to the test sample, and we can use this weighted linear combination to perform robust classification. Experimental results conducted on the ORL database of faces (created by the Olivetti Research Laboratory in Cambridge), the FERET face database (managed by the Defense Advanced Research Projects Agency and the National Institute of Standards and Technology), AR face database (created by Aleix Martinez and Robert Benavente in the Computer Vision Center at U.A.B), and USPS handwritten digit database (gathered at the Center of Excellence in Document Analysis and Recognition at SUNY Buffalo) demonstrate the effectiveness of the proposed method.

A twice face recognition algorithm

The theory of compressive sensing applies the sparse representation to the extraction of useful information from signals and brings a breakthrough to the theory of signal sampling. Based on compressive sensing, sparse representation-based classification (SRC) is proposed. SRC uses the compressibility of the image data to represent the facial image sparsely and could solve the problems of both massive calculation and information loss in dealing with signals. SRC does not, however, deal with the effects of variable illumination, posture and incomplete face image, which could result in severe performance degradation. This paper studies the differences between SRC recognition and human recognition. We find that there is an obvious disadvantage in the SRC algorithm, and it will significantly affect the face recognition performance in actual environment, especially for the variable illumination, posture and incomplete face image. To overcome the disadvantage of SRC algorithm, we propose an SRC-based twice face recognition algorithm named T_SRC. T_SRC uses bidirectional PCA, linear discriminant analysis and GradientFace to execute multichannel analysis, which could extract more "holistic/configural" face features in actual environment than by using SRC algorithm directly. Based on the multichannel analysis, we identify the test image by SRC firstly. Then, by analyzing the residual, this algorithm could decide whether the twice recognition is needed. If the twice recognition is needed, T_SRC extracts the facial details ("featural" face features) by the improved Harris point and Gabor filter detector. We suppose that the facial details are more stable than the whole face in actual environment, and later experiments verify our assumption. At last, this algorithm identifies the class of the test image by SRC again. The results of the experiments prove that the T_SRC algorithm has better recognition rate than SRC.

Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class. To compensate for the missing illumination information traditionally provided by multiple gallery images, a sparse illumination learning and transfer (SILT) technique is introduced. The illumination in SILT is learned by fitting illumination examples of auxiliary face images from one or more additional subjects with a sparsely-used illumination dictionary. By enforcing a sparse representation of the query image in the illumination dictionary, the SILT can effectively recover and transfer the illumination and pose information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the state of the art in the single-sample regime and with less restrictions. In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class. Furthermore, the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

Sparse representation for face recognition by discriminative low-rank matrix recovery

This paper proposes a discriminative low-rank representation (DLRR) method for face recognition in which both the training and test samples are corrupted owing to variations in occlusion and disguise. The proposed method extends the sparse representation-based classification algorithm by incorporating the low-rank structure of data representation. The DLRR algorithm recovers a clean dictionary with enhanced discrimination ability from the corrupted training samples for sparse representation. Simultaneously, it learns a low-rank projection matrix to correct corrupted test samples by projecting them onto their corresponding underlying subspaces. The dictionary elements from different classes are encouraged to be as independent as possible by regularizing the structural incoherence of the original training samples. This leads to a compact representation of a corrected test sample by a linear combination of more dictionary elements from the corrected class. The experimental results on benchmark databases show the effectiveness and robustness of our face recognition technique.

Improving Hyperspectral Image Classification Using Spectral Information Divergence

In order to improve the classification performance for hyperspectral image (HSI), a sparse representation classifier based on spectral information divergence (SID) is proposed. SID measures the discrepancy of probabilistic behaviors between the spectral signatures of two pixels from the aspect of information theory, which can be more effective in preserving spectral properties. Thus, the new method measures the similarity between the reconstructed pixel and the true pixel by SID instead of by the L2 norm used in traditional sparse model. Moreover, the spatial coherency across neighboring pixels sharing a common sparsity pattern is taken into account during the construction of SID-based joint sparse representation model. We propose a new version of the orthogonal matching pursuit method to solve SID-based recovery problems. The proposed SID-based algorithms are applied to real HSI for classification. Experimental results show that our algorithms outperform the classical sparse representation based classification algorithms in most cases.

Sparse representation-based classification algorithm for optical Tibetan character recognition

A sparse representation-based two-phase classification algorithm is proposed for off-line handwritten Tibetan character recognition. The first phase realizes coarse classification with the K-NN classifier by finding the K nearest neighbours of a test sample in the dictionary constructed by K-SVD with samples of all classes, and the classes of these neighbours are regarded as the candidate classes of the test sample. The second phase performs fine classification with the sparse representation classifier by sparsely representing the test sample with all elements of the dictionary constructed by K-SVD with samples of all candidate classes, and the test sample is finally classified into the candidate class with the maximal contribution in sparse representation. Experiments on the Tibetan off-line handwritten character database show that an optimal recognition rate of 97.17% has been reached and it is 2.12% higher than that of K-NN.

A new sparse representation-based classification algorithm using iterative class elimination

In this paper, we propose a sparse representation-based classification method using iterative class elimination strategy for face recognition. The proposed method aims to represent a test sample as a linear combination of the most competitive training samples and exploits an optimal representation of training samples from the classes with major relevant contributions. We interpret the sparse representation problem as an information fidelity problem. In the context of our proposed method, an important goal is to select a subset of variables for accomplishing one objective: the provision of a descriptive representation for sparse class knowledge structure. We develop an iterative class elimination algorithm to achieve this goal. First, the contribution in presenting the test sample of any of the specified classes is, respectively, calculated by adding up the total contribution of all training samples of this class, and then a certain class that meets the smallest score requirement to this test sample is eliminated. Second, a similar procedure is iteratively carried out for the set of remaining training samples from rest classes, and this procedure is repeatedly performed till the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to classify it. Therefore, the proposed algorithm is an iterative method that alternates between sparse representation and a process of updating the training atoms to better fit the test data. This is helpful to accurately classify the test sample. Experimental results conducted on the ORL, FERET, and AR face databases demonstrate the effectiveness of the proposed method.

Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary

Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a single, training images per subject. Assuming that the intraclass variations of one subject can be approximated by a sparse linear combination of those of other subjects, Extended Sparse Representation-Based Classifier (ESRC) applies an auxiliary intraclass variant dictionary to represent the possible variation between the training and testing images. The dictionary atoms typically represent intraclass sample differences computed from either the gallery faces themselves or the generic faces that are outside the gallery. Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages. The superior results of ESRC suggest that if the dictionary is properly constructed, SRC algorithms can generalize well to the large-scale face recognition problem, even with a single training image per class.

Robust Hierarchical Framework for Image Classification via Sparse Representation

The sparse representation-based classification algorithm has been used for human face recognition. But an image database was restricted to human frontal faces with only slight illumination and expression changes. Cropping and normalization of the face needs to be done beforehand. This paper uses a sparse representation-based algorithm for generic image classification with some intra-class variations and background clutter. A hierarchical framework based on the sparse representation is developed which flexibly combines different global and local features. Experiments with the hierarchical framework on 25 object categories selected from the Caltech101 dataset show that exploiting the advantage of local features with the hierarchical framework improves the classification performance and that the framework is robust to image occlusions, background clutter, and viewpoint changes.

Sparse Based Image Classification With Bag-of-Visual-Words Representations

The sparse representation based classification algorithm has been used to solve the problem of human face recognition, but the image database is restricted to human frontal faces with only slight illumination and expression changes. This paper applies the sparse representation based algorithm to the problem of generic image classification, with a certain degree of intra-class variations and background clutter. Experiments are conducted with the sparse representation based algorithm and Support Vector Machine (SVM) classifiers on 25 object categories selected from the Caltech101 dataset. Experimental results show that without the time-consuming parameter optimization, the sparse representation based algorithm achieves comparable performance with SVM. The experiments also demonstrate that the algorithm is robust to a certain degree of background clutter and intra-class variations with the bag-of-visual-words representations. The sparse representation based algorithm can also be applied to generic image classification task when the appropriate image feature is used.