Joint Sparse Representation Research Articles

Underground Coal Mine Target Tracking via Multi-Feature Joint Sparse Representation

Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video. In this study, we propose an underground coal mine personnel target tracking method using multi-feature joint sparse representation. First, with a particle filter framework, the global and local multiple features of the target template and candidate particles are extracted. Second, each of the candidate particles is sparsely represented by a dictionary template, and reconstruction is achieved after solving the sparse coefficient. Last, the particle with the lowest reconstruction error is deemed the tracking result. To validate the effectiveness of the proposed algorithm, we compare the proposed method with three commonly employed tracking algorithms. The results show that the proposed method is able to reliably track the target in various scenarios, such as occlusion and illumination change, which generates better tracking results and validates the feasibility and effectiveness of the proposed method.

Bilateral Joint-Sparse Regression for Hyperspectral Unmixing

Sparse hyperspectral unmixing has been a hot topic in recent years. Joint sparsity assumes that each pixel in a small neighborhood of hyperspectral images (HSIs) is composed of the same endmembers, which results in a few nonzero rows in the abundance matrix. Recall that a plethora of unmixing algorithms transform a 3-D HSI into a 2-D matrix with vertical priority. The transformation makes matrix computation easier. It is, however, hard to maintain the horizontal spatial information in HSIs in many cases. To make further use of the spatial information of HSIs, in this article, we propose a bilateral joint-sparse structure for hyperspectral unmixing in an attempt to exploit the local joint sparsity of the abundance matrix in both the vertical and horizontal directions. In particular, we introduce a permutation matrix to realize the bilateral joint-sparse representation and there is no need to construct the matrix explicitly. Moreover, we propose to simultaneously impose the bilateral joint-sparse structure and low rankness on the abundance and develop a new algorithm named bilateral joint-sparse and low-rank unmixing . The proposed algorithm is based on the alternating direction method of multipliers framework and employs a reweighting strategy. The convergence analysis of the proposed algorithm is investigated. Simulated and real-data experiments show the effectiveness of the proposed algorithm.

Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data

Considering the defaults in synthetic aperture radar (SAR) image feature extraction, an SAR target recognition method based on non-subsampled Shearlet transform (NSST) was proposed with application to target recognition. NSST was used to decompose an SAR image into multilevel representations. These representations were translation-invariant, and they could well reflect the dominant and detailed properties of the target. During the machine learning classification stage, the joint sparse representation was employed to jointly represent the multilevel representations. The joint sparse representation could represent individual components independently while considering the inner correlations between different components. Therefore, the precision of joint representation could be enhanced. Finally, the target label of the test sample was determined according to the overall reconstruction error. Experiments were conducted on the MSTAR dataset to examine the proposed method, and the results confirmed its validity and robustness under the standard operating condition, configuration variance, depression angle variance, and noise corruption.

Block sparse representation approach to 2D DOA and polarisation estimation of wideband signals using a sparse vector antenna array

In this study, a multi-polarised two-dimensional (2D) planar array of sparsely located vector antennas (VAs) is designed for the block sparse representation (SR)-based 2D direction finding and polarisation parameter estimation of wideband signals. In order to alleviate the inter-VA mutual coupling effect, the minimum inter-VA spacing of the 2D sparse array is constrained to be no less than one wavelength that corresponds to the highest signal frequency. To reduce the computational complexity of parameter estimation, the 2D block SR model for the 2D difference coarray output at a certain frequency bin is established, under which the two direction cosine terms for 2D direction-of-arrival (DOA) estimation are decoupled with the polarimetric terms. This enables separated but simultaneous 2D direction finding and polarisation parameter estimation with the newly developed joint 2D block orthogonal matching pursuit (Joint-2D-BOMP) subband fused sparse recovery algorithm. Moreover, with the use of two spatial only (polarisation independent) over-complete dictionaries, the representation dimension of the new 2D block SR model is greatly reduced as compared with the traditional space-polarisation joint SR model. The efficacy of the presented VA array geometry and the associated parameter estimation method is validated by computer simulations.

Selection of multi-view SAR images via nonlinear correlation information entropy with application to target classification

ABSTRACT A synthetic aperture radar (SAR) target classification method is proposed by properly selecting multiple views via the nonlinear correlation information entropy (NCIE). The optimal subset of multi-view SAR images are selected, which are assumed to share stable inner correlations. The joint sparse representation is adopted as the basic classification scheme for the selected views to exploit their inner correlations. According to the total reconstruction error of the selected multi-view SAR images, the target label can be decided. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is employed to test the proposed method and the results validate the its superiority over some reference methods.

Target classification of SAR images using nonlinear correlation information entropy

A synthetic aperture radar (SAR) target classification method is proposed by selecting deep features learned by convolutional neural network (CNN) using the nonlinear correlation information entropy (NCIE). The deep feature maps are learned from original SAR images by CNN. Considering the possible indiscrimination and redundancy, all the deep feature maps from different layers are analyzed by NCIE to select out those discriminative ones. The selected feature maps share high inner correlations, which are jointly classified based on joint sparse representation (JSR). Finally, the target label is determined based on the reconstruction errors from JSR. The famous moving and stationary target acquisition and recognition dataset are employed to setup the standard operating condition and several extended operating conditions, including configuration variants, depression angle differences, noise corruption, and partial occlusion, to comprehensively evaluate the performance of the proposed method. The results confirm the superior effectiveness and robustness of the proposed method in comparison with some state-of-the-art SAR target classification methods.

Downlink channel estimation for millimeter wave communication combining low-rank and sparse structure characteristics

The acquisition of channel state information (CSI) is essential in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The mmWave channel exhibits sparse scattering characteristics and a meaningful low-rank structure, which can be simultaneously employed to reduce the complexity of channel estimation. Most existing works recover the low-rank structure of channels using nuclear norm theory. However, solving the nuclear norm-based convex problem often leads to a suboptimal solution of the rank minimization problem, thus degrading the accuracy of channel estimation. Previous contributions recover the channel using over-complete dictionary with the assumption that the mmWave channel can be sparsely represented under some dictionary. While over-complete dictionary may increase the computational complexity. To address these problems, we propose a channel estimation framework based on non-convex low-rank approximation and dictionary learning by exploring the joint low-rank and sparse representations of wireless channels. We surrogate the widely used nuclear norm theory with non-convex low-rank approximation method and design a dictionary learning algorithm based on channel feature classification employing deep neural network (DNN). Our simulation results reveal the proposed scheme outperform the conventional dictionary learning algorithm, Bayesian framework algorithm, and compressed sensing-based algorithms.

Two-stage sparse representation of NSCT features with application to SAR target classification

To improve synthetic aperture radar (SAR) target classification performance, both the feature extraction and classification algorithms are considered. The nonsubsampled contourlet transform (NSCT) is employed to decompose SAR images to generate multi-layer components, which provide more discriminative descriptions for the targets. During the classification, the two-stage sparse representations are developed. Each NSCT component is classified using the sparse representation-based classification (SRC). Afterwards, the joint sparse representation (JSR) is adopted to represent the selected NSCT components from the first stage, which are assumed to be highly discriminative. Based on the two decisions, the target label of the test sample is determined. In the experiment, the moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up scenarios for performance evaluation including the standard operating condition (SOC) and several extended operating conditions (EOCs). The results show the superior performance of the proposed methodover some current methods.

Image Target Recognition via Mixed Feature-Based Joint Sparse Representation.

An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data-lightweight classification framework, which can recognize targets well with few training samples. First, Gabor wavelet transform and convolutional neural network (CNN) are used to extract the Gabor wavelet features and deep features of training samples and test samples, respectively. Then, the contribution weights of the Gabor wavelet feature vector and the deep feature vector are calculated. After adaptive weighted reconstruction, we can form the mixed features and obtain the training sample feature set and test sample feature set. Aiming at the high-dimensional problem of mixed features, we use principal component analysis (PCA) to reduce the dimensions. Lastly, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on joint feature dictionary, the sparse representation based classifier (SRC) is used to recognize the targets. The experiments on different datasets show that this approach is superior to some other advanced methods.

Joint Reflectance Field Estimation and Sparse Representation for Face Image Illumination Preprocessing and Recognition

Illumination preprocessing is an important ingredient for handling lighting variation face recognition challenge. Nonetheless, existing methods are usually designed to be independent of the face recognition methods and the interaction between them is not yet well explored. In this paper, we formulate the face image illumination preprocessing and recognition into a unified sparse representation framework and propose a novel joint reflectance field estimation and sparse representation (JRSR) method for face recognition under extreme lighting conditions. The proposed method separates the identify factor and the interfered illumination of a query sample simultaneously by one nonconvex sparse optimizing model. We also present an efficient approximation algorithm to solve JRSR in this paper. Evaluation on several face databases and the experimental results of face recognition with illumination variation clearly demonstrate the advantages of our proposed JRSR algorithm in illumination preprocessing efficiency and recognition accuracy.

Centralized joint sparse representation for multi-view subspace clustering

Multi-view subspace clustering arises in many computer visional tasks such as object recognition and image segmentation. The basic idea is to measure the same instance with multiple views. In this paper, we proposed two centralized joint sparse repre

Learning Low-Rank Sparse Representations With Robust Relationship Inference for Image Memorability Prediction

Image memorability prediction aims to estimate the degree to which an image will be remembered by observers. Generally, the core problem in image memorability prediction is how to obtain effective representations to characterize the visual content of an image. In contrast to existing methods, which focus more on exploring the factors that make images memorable, in this paper, we first propose a general framework for learning joint low-rank and sparse principal feature representations, called the LSPFR framework, to obtain the lowest-rank intrinsic representation for image memorability prediction. By considering the joint optimization of the nuclear and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norms, the global low-rank structure and the local patterns embedded in data can be exploited to make the learned features more robust and informative. To improve our framework based on the exploitation of sample relationship structure information, we present an extended version of LSPFR, named E-LSPFR, in which the underlying relationship structure matrix is inferred through a negative log-likelihood term with a sparsity constraint. The results of experiments conducted on four publicly available datasets confirm the superior performance of our proposed approaches.

Learning Joint and Specific Patterns: A Unified Sparse Representation for Off-the-Person ECG Biometric Recognition

Devices such as smartphones and tablets have spurred interest in off-the-person electrocardiogram (ECG) biometric recognition. While the advantage of using multi-feature information for establishing identities has been widely recognized, computational sparse representation models for multi-feature biometric recognition have only recently received more attention. We propose a unified sparse representation framework which collaboratively exploits joint and specific patterns for ECG biometric recognition. In particular, unlike joint sparse representation, which only considers the consistency among sparsity patterns of multiple features, we combine the consistent and pairwise constraints, which not only learn latent discriminant representations for all features but capture the interactions between them. In addition, our framework is universal and easily adapts to other multi-feature sparse representation models by just tuning the regularization parameters. The optimization problem is solved by an efficient alternating direction method of multipliers (ADMM). Extensive experiments on two publicly available off-the-person datasets demonstrate that our method can achieve competitive or even superior performance compared to state-of-the-art ECG biometric recognition methods.

Direct data domain space-time adaptive processing method based on joint sparse representation with single snapshot for MIMO radar

To improve the performance of exiting sparse recovery-based space-time adaptive processing (STAP) methods in nonstationary and heterogeneous environments, a STAP method, called the joint sparse representation (JSR)-based direct data domain STAP method using MIMO subarraying radar, is proposed. First, by exploiting the property of waveforms orthogonal diversity of MIMO subarraying radar, the received single snapshot signal of the range cell under test is expressed as multiple snapshot signals. Second, the JSR model of clutter plus target spatiotemporal spectrum estimation is established and the joint sparsity is proved. The spatiotemporal spectrum of clutter and target is then estimated by the typical JSR algorithm. At last, the clutter covariance clutter matrix and the corresponding weight vector of the STAP processor are calculated based on the clutter space-time distribution extracted by the rough priori knowledge of the target. By utilizing the data of the range cell under test only, the proposed method could achieve accurate spatiotemporal spectrum estimation and great performance of clutter suppression. Experimental simulations demonstrate the validity of the proposed method.

Histopathological image classification through discriminative feature learning and mutual information-based multi-channel joint sparse representation

Histopathological image classification is a very challenging task because of the biological heterogeneities and rich geometrical structures. In this paper, we propose a novel histopathological image classification framework, which includes the discriminative feature learning and the mutual information-based multi-channel joint sparse representation. We first propose a stack-based discriminative prediction sparse decomposition (SDPSD) model by incorporating the class labels information to predict deep discriminant features automatically. Subsequently, a mutual information-based multi-channel joint sparse model (MIMCJSM) is presented to jointly encode the common component and particular components of the discriminative features. Especially, the main advantage of the MIMCJSM is the construction of a joint dictionary using a mutual information criterion, which contains a common sub-dictionary and three particular sub-dictionaries. Based on the joint dictionary, the MIMCJSM captures the relationship of multi-channel features, which can improve discriminative ability of joint sparse representation coefficients. Finally, the joint sparse representation coefficients of different levels can be aggregated using the spatial pyramid matching (SPM) model, and the linear support vector machine (SVM) is used as the classifier. Experimental results on ADL and BreaKHis datasets demonstrate that our proposed framework consistently performs better than popular existing classification frameworks. Additionally, it can show promising strong-robustness performance for histopathological image classification.

Joint sparse representation of hyperspectral image classification based on secondary dictionary

Joint sparse representation of hyperspectral image classification based on secondary dictionary

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter.

Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information ()—5.3377, feature mutual information ()—0.5600, normalized weighted edge preservation value ()—0.6978 and nonlinear correlation information entropy ()—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification.

Noisy Remote Sensing Image Fusion Based on JSR

Compressed sensing has shown great potential and power in image representation, especially in image reconstruction by sparse representation. Due to complementary information and unavoidable noise existing in synthetic aperture radar (SAR) and other source images, joint sparse representation (JSR) is developed to separate redundancy and complementary information with different properties in source images and obtain a fused image, where image de-noising is done simultaneously owing to that noise is not sparse and cannot be represented by sparse representation. As a result, one noisy remote sensing image fusion method based on JSR is presented in this paper. After obtaining redundant and complementary sub-images by JSR, an improved fusion rule based on pulse coupled neural network (PCNN) is employed to fuse complementary sparse coefficients together. At the same time, because the types of noise in SAR and other source images are different, they can be treated as the complementary information in source images and suppressed at this step. Finally, a fused image can be reconstructed by adding the redundant and fused complementary sub-images. Quantitative and qualitative experimental results show that the proposed method outperforms most of other fusion methods and it is more robust to noise, having better visual effects and values of objective evaluation metrics.

Selection of Multi-Level Deep Features via Spearman Rank Correlation for Synthetic Aperture Radar Target Recognition Using Decision Fusion

Convolutional neural networks (CNN) now become one of the most popular methods in synthetic aperture radar (SAR) target recognition. To fully exploit the deep features learned by CNN, this paper considers all the feature maps from different convolution layers. At each layer, the Spearman rank correlation is employed to evaluate the similarities between the feature maps and original SAR image. A certain proportion of feature maps with high similarities are selected and jointly represented based on the joint sparse representation (JSR) model. For the reconstruction error vectors from different layers, they are combined based on linear weighting using a random weight matrix. The fused reconstruction errors are analyzed to form a decision value for target recognition. The feature selection chooses the robust features and JSR considers the inner correlations between the feature maps from the same layer. In addition, the linear weighting using the random weight matrix could statistically reveal the correlations between the test sample and a certain training class. Therefore, the overall effectiveness and robustness of the proposed method can be enhanced. By performing experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset, the proposed method could achieve a very high average recognition rate of 99.32% for ten classes of ground targets under the standard operating condition (SOC). Furthermore, under the extended operating conditions (EOCs) like configuration differences, depression angle differences, noise corruption, and partial occlusion, the proposed could also achieve superior robustness over some state-of-the-art SAR target recognition methods.

A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification

Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important study areas for PolSAR image processing. Many kinds of PolSAR features can be extracted for PolSAR image classification, such as the scattering, polarimetric or image features. However, it is difficult to improve the classification accuracy of PolSAR images by using all these low-level features directly, since they may conflict with each other for classification. Hence, how to joint learn these low-level features to obtain high-level discriminating features is a challenging task. To solve this problem, a novel fast multi-feature joint learning method(fMF-JLC) is proposed for PolSAR image classification. The proposed method extract three kinds of low-level features of PolSAR data at first. Then, a multi-feature joint sparse representation model(MF-JSR) is proposed by designing joint sparse constraints on the extracted features above. Moreover, the joint sparse features are further compressed to overcome the dimension curse and acquire semantic features by the topic model. By this way, the low-level features are fused and discriminating high-level features are acquired. However, the pixel-wise feature learning method is time consuming. To speed the proposed method, a superpixel-based fast learning method is designed by involving the contextual relationship. Experiments are taken on three sets of real PolSAR data with different sensors and bands, and several compared methods are used to verify the effectiveness of the proposed method. The experimental results illustrate that the proposed method can obtain better performance than the state-of-art methods, especially for the heterogenous areas.