Local Sparse Representation Research Articles

Multi-level dynamic error coding for face recognition with a contaminated single sample per person

Single sample per person (SSPP) has always been a significantly challenging and practical problem for face recognition owing to limited information and facial variations. Although the existing holistic and local methods have achieved great success in face recognition with SSPP, their performances suffer a serious degradation when SSPP is accompanied by a contaminated gallery database. In this situation, the gallery set is usually disturbed by facial variations such as illumination, expression, and disguise. To solve this problem, we propose a novel method called multi-level dynamic error coding. First, a multi-level pyramid structure is constructed for holistic and local sparse representation, where gallery dictionary patches are extracted to build a local gallery dictionary and a variation dictionary is built by extracting the generic dataset patches to depict potential facial variations. Second, we further propose a scheme of dynamic error coding by constructing an error function at different levels to reduce the negative impact of variations. At last, the corrected holistic and local errors are fused to perform the classification. Experimental results on various benchmark data sets have demonstrated that our method has strong generalization ability to dictionary and is more robust against facial variations under SSPP.

Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image.

Hyperspectral image (HSI) generally contains a complex manifold structure and strong sparse correlation in its nonlinear high-dimensional data space. However, the existing manifold learning and sparse learning methods usually consider the manifold structure and sparse relationship separately rather than combining manifold and sparse properties to discover the intrinsic information in the original data. To simultaneously reveal the complex sparse relation and manifold structure of HSI, a novel feature extraction (FE) method, called local manifold-based sparse discriminant learning (LMSDL), has been proposed on the basis of manifold learning and sparse representation (SR). The LMSDL method first designs a new sparse optimization model called local manifold-based SR (LMSR) to reveal the local manifold-based sparse structure of data. Then, two geometrical sparse graphs are constructed to represent the discriminant relationship between samples and the geometrical and sparse neighbors. An objective function is constructed via geometrical sparse graphs and reconstruction points to learn a projection matrix for FE. The LMSDL effectively reveals the complex sparse relation and manifold structure in high-dimensional data, and it enhances the representation ability of extracted features for HSI classification significantly. The experimental results on the three real HSI datasets show that the proposed LMSDL algorithm possesses better performance in comparison with some state-of-the-art FE methods.

Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning.

In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL) framework for the discriminative sparse representation (SR) learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective DPL, locality-adaptive SRs, and discriminative coding coefficients learning into a unified learning framework. Specifically, RA-DPL improves existing projective DPL in four perspectives. First, it applies a sparse l2,1 -norm-based metric to encode the reconstruction error to deliver the robust projective dictionary pairs, and the l2,1 -norm has the potential to minimize the error. Second, it imposes the robust l2,1 -norm clearly on the analysis dictionary to ensure the sparse property of the coding coefficients rather than using the costly l0/l1 -norm. As such, the robustness of the data representation and the efficiency of the learning process are jointly considered to guarantee the efficacy of our RA-DPL. Third, RA-DPL conceives a structured reconstruction weight learning paradigm to preserve the local structures of the coding coefficients within each class clearly in an adaptive manner, which encourages to produce the locality preserving representations. Fourth, it also considers improving the discriminating ability of coding coefficients and dictionary by incorporating a discriminating function, which can ensure high intraclass compactness and interclass separation in the code space. Extensive experiments show that our RA-DPL can obtain superior performance over other state of the arts.

Foreground Extraction and Motion Recognition Technology for Intelligent Video Surveillance

With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background modeling technique based on video block is proposed. The background is modeled at the block level, which greatly reduces the spatial complexity of the algorithm. It solves the problem that the traditional Gaussian model (GMM) moving target enters the static state and is integrated into the background process. The target starts to move for a long time and there are ghosts and other problems, which reduce the processing efficiency of the lifting algorithm. The test results on the Weizmann dataset show that the proposed algorithm can achieve high human motion recognition accuracy and recognition with low computational complexity. The rate can reach 100%; the local constrained group sparse representation classification (LGSRC) model is used to classify it. The experimental results on Weizmann, KTH, UCF sports and other test datasets confirm the validity of the algorithm in this chapter. KNN, SRC voting classification accuracy.

Improving Spectral-Based Endmember Finding by Exploring Spatial Context for Hyperspectral Unmixing

Hyperspectral unmixing, which intends to decompose mixed pixels into a collection of endmembers weighted by their corresponding fraction abundances, has been widely utilized for remote sensing image exploitation. Recent studies have revealed that spatial context of pixels is important complemental information for hyperspectral image processing. However, many well-known endmember finding (EF) algorithms identify spectrally pure spectra from hyperspectral images according to spectral information only, resulting in limited accuracy of hyperspectral unmixing application since they ignore spatial distribution or structure information in the image. Therefore, in this article, several novel spatial exploiting (SE) strategies are proposed to improve the performance of the well-known spectral-based EF (sEF) algorithms by integrating spatial information. Three different spatial exploiting strategies are designed to use pixel spatial context, by which the spectral variation of pixels can be alleviated to improve the performance of hyperspectral unmixing. Specifically, in pixel domain, the pixels are linearly reconstructed using their neighbors in which the spatially derived factor to weight the importance of the spectral information is generated using local linear representation and local sparse representation, while in the feature domain, pixels are revised using dominated features of neighboring pixels in singular value decomposition. The proposed spatial exploiting strategies can not only be used as a preprocessing stage to revise pixels for sEF algorithms, but also be used as a postprocessing step to revise endmembers found via sEF algorithms. Finally, experimental results on both synthetic and real hyperspectral datasets demonstrate that the proposed SE strategies can certainly improve the performance of several well-known sEF algorithms, and obtain more accurate unmixing results than several state-of-the-art spatial preprocessing methods.

Hierarchical Discriminative Deep Dictionary Learning

In deep dictionary learning multiple dictionaries are learned based on information at various levels of abstraction. We propose a novel hierarchical discriminative dictionary learning layer embedded within a neural network with an image classification objective. Discrimination is induced in the learned synthesis dictionaries at multiple hierarchical levels in a simple way by a one-hot-code representation of the class labels during the training backward pass. In addition, local sparse representation objectives are approximated during the forward pass, introducing local regularization. We evaluate our proposal on five known datasets and we either outperform state-of-the-art methods or achieve competitive classification results.

Hyperspectral image classification by combining local binary pattern and joint sparse representation

ABSTRACTThis paper proposes a new hyperspectral classification method that combines the joint sparse representation classifier (JSRC) and local binary pattern (LBP) (JSR-LBP). The proposed method uses a statistical histogram of the LBP feature spectrum as a feature vector for classification and recognition. Using the LBP method let us extract the local texture feature of an image more accurately and effectively because of the forceful robustness to light. The proposed method JSR-LBP mainly includes the following steps: First, the JSRC is used to obtain the representation residuals of different pixels. Then, the LBP value is calculated for every pixel in the whole image to generate the LBP histogram; the LBP histogram of the test sample can be obtained from a series of binary codes that are generated using statistics. Next, the Bhattacharyya coefficient is employed to measure the similarity between the test and training samples unlike the traditional classifiers as -nearest neighbor that usually employ the Euclidean distance as similarity metric, and a regularization parameter is then introduced to achieve the balance between the JSRC and LBP. Finally, the test pixel’s label is determined by applying the final residual. Experimental results performed on three real hyperspectral images data demonstrate the outstanding performance of the proposed approach compared to other broader classifiers.

Multi-feature local sparse representation for infrared pedestrian tracking

Multi-feature local sparse representation for infrared pedestrian tracking

Robust particle tracking via spatio-temporal context learning and multi-task joint local sparse representation

Particle filters have been proven very successful for non-linear and non-Gaussian estimation problems and extensively used in object tracking. However, high computational costs and particle decadency problem limit its practical application. In this paper, we present a robust particle tracking approach based on spatio-temporal context learning and multi-task joint local sparse representation. The proposed tracker samples particles according to the confidence map constructed by the spatio-temporal context information of the target. This sampling strategy can ameliorate problems of sample impoverishment and particle degeneracy, target state distribution to obtain robust tracking performance. In order to locate the target more accurately and be less sensitive to occlusion, the local sparse appearance model is adopted to capture the local and structural information of the target. Finally, the multi-task learning where the representations of particles are learned jointly is employed to further improve tracking performance and reduce overall computational complexity. Both qualitative and quantitative evaluations on challenging benchmark image sequences have demonstrated that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Visual Tracking With Weighted Adaptive Local Sparse Appearance Model via Spatio-Temporal Context Learning.

Sparse representation has been widely exploited to develop an effective appearance model for object tracking due to its well discriminative capability in distinguishing the target from its surrounding background. However, most of these methods only consider either the holistic representation or the local one for each patch with equal importance, and hence may fail when the target suffers from severe occlusion or large-scale pose variation. In this paper, we propose a simple yet effective approach that exploits rich feature information from reliable patches based on weighted local sparse representation that takes into account the importance of each patch. Specifically, we design a reconstruction-error based weight function with the reconstruction error of each patch via sparse coding to measure the patch reliability. Moreover, we explore spatio-temporal context information to enhance the robustness of the appearance model, in which the global temporal context is learned via incremental subspace and sparse representation learning with a novel dynamic template update strategy to update the dictionary, while the local spatial context considers the correlation between the target and its surrounding background via measuring the similarity among their sparse coefficients. Extensive experimental evaluations on two large tracking benchmarks demonstrate favorable performance of the proposed method over some state-of-the-art trackers.

Toward a Blind Quality Predictor for Screen Content Images

Blind quality assessment of screen content images (SCIs) is much challenging than traditional natural images. In this paper, we propose a blind quality predictor for SCIs to explore the issue from the perspective of sparse representation. Specifically, we conduct local sparse representation for the textual and pictorial regions, respectively, and conduct global sparse representation for the global SCIs. Subsequently, the underlying relationship between the feature and quality vectors is bridged in a universal sparse representation framework. The quality pooling is comparatively simply that only need to estimate the local and global quality scores and combine them to a total one. Our experimental results show that the proposed predictor can achieve better prediction performance to be in line with subjective assessment.

WLDISR: Weighted Local Sparse Representation-Based Depth Image Super-Resolution for 3D Video System.

In this paper, we propose a Weighted Local sparse representation based Depth Image Super-Resolution (WLDISR) schemes aiming at improving the Virtual View Image (VVI) quality of 3D video system. Different from color images, depth images are mainly used to provide geometrical information in synthesizing VVI. Due to the view synthesis characteristics difference between textural structures and smooth regions of depth images, we divide the depth images into edge and smooth patches and learn two local dictionaries, respectively. Meanwhile, the weight term is derived and incorporated explicitly in the cost function to denote different importance of edge structures and smooth regions to the VVI quality. Then, local sparse representation and weighted sparse representation are jointly used in both dictionary learning and reconstruction phases in depth image super-resolution. Based on different optimizations on learning and reconstruction modules, three WLDISR schemes, WLDISR-D, WLDISR-R, and WLDISR-ALL, are proposed. Experimental results on 3D sequences demonstrate that the proposed WLDISR-D, WLDISR-R, and WLDISR-ALL schemes can achieve more than 1.9-, 2.03-, and 2.16-dB gains on average, respectively, in terms of the VVIs' quality, as compared with the state-of-the-art schemes. In addition, the visual quality of VVIs is also improved.

Visual Tracking Using Sparse Coding and Earth Mover's Distance.

An efficient iterative Earth Mover's Distance (iEMD) algorithm for visual tracking is proposed in this paper. The Earth Mover's Distance (EMD) is used as the similarity measure to search for the optimal template candidates in feature-spatial space in a video sequence. The local sparse representation is used as the appearance model for the iEMD tracker. The maximum-alignment-pooling method is used for constructing a sparse coding histogram which reduces the computational complexity of the EMD optimization. The template update algorithm based on the EMD is also presented. When the camera is mounted on a moving robot, e.g., a flying quadcopter, the camera could experience a sudden and rapid motion leading to large inter-frame movements. To ensure that the tracking algorithm converges, a gyro-aided extension of the iEMD tracker is presented, where synchronized gyroscope information is utilized to compensate for the rotation of the camera. The iEMD algorithm's performance is evaluated using eight publicly available videos from the CVPR 2013 dataset. The performance of the iEMD algorithm is compared with eight state-of-the-art tracking algorithms based on relative percentage overlap. Experimental results show that the iEMD algorithm performs robustly in the presence of illumination variation and deformation. The robustness of this algorithm for large inter-frame displacements is also illustrated.

Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation

Appearance models play an important role in visual tracking. Effective modeling of the appearance of tracked objects is still a challenging problem because of object appearance changes caused by factors, such as partial occlusion, illumination variation and deformation, etc. In this paper, we propose a tracking method based on the patch descriptor and the structural local sparse representation. In our method, the object is firstly divided into multiple non-overlapped patches, and the patch sparse coefficients are obtained by structural local sparse representation. Secondly, each patch is further decomposed into several sub-patches. The patch descriptors are defined as the proportion of sub-patches, of which the reconstruction error is less than the given threshold. Finally, the appearance of an object is modeled by the patch descriptors and the patch sparse coefficients. Furthermore, in order to adapt to appearance changes of an object and alleviate the model drift, an outlier-aware template update scheme is introduced. Experimental results on a large benchmark dataset demonstrate the effectiveness of the proposed method.

Local robust sparse representation for face recognition with single sample per person

The purpose of this paper is to solve the problem of robust face recognition U+0028 FR U+0029 with single sample per person U+0028 SSPP U+0029. In the scenario of FR with SSPP, we present a novel model local robust sparse representation U+0028 LRSR U+0029 to tackle the problem of query images with various intra-class variations, e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images. The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intraclass variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.

SAR Target Recognition via Local Sparse Representation of Multi-Manifold Regularized Low-Rank Approximation

The extraction of a valuable set of features and the design of a discriminative classifier are crucial for target recognition in SAR image. Although various features and classifiers have been proposed over the years, target recognition under extended operating conditions (EOCs) is still a challenging problem, e.g., target with configuration variation, different capture orientations, and articulation. To address these problems, this paper presents a new strategy for target recognition. We first propose a low-dimensional representation model via incorporating multi-manifold regularization term into the low-rank matrix factorization framework. Two rules, pairwise similarity and local linearity, are employed for constructing multiple manifold regularization. By alternately optimizing the matrix factorization and manifold selection, the feature representation model can not only acquire the optimal low-rank approximation of original samples, but also capture the intrinsic manifold structure information. Then, to take full advantage of the local structure property of features and further improve the discriminative ability, local sparse representation is proposed for classification. Finally, extensive experiments on moving and stationary target acquisition and recognition (MSTAR) database demonstrate the effectiveness of the proposed strategy, including target recognition under EOCs, as well as the capability of small training size.

基于局部稀疏形状表示的医学图像分割模型

基于局部稀疏形状表示的医学图像分割模型

Gesture recognition based on an improved local sparse representation classification algorithm

The sparse representation classification method has been widely concerned and studied in pattern recognition because of its good recognition effect and classification performance. Using the minimized $$l_{1}$$ norm to solve the sparse coefficient, all the training samples are selected as the redundant dictionary to calculate, but the computational complexity is higher. Aiming at the problem of high computational complexity of the $$l_{1}$$ norm based solving algorithm, $$l_{2}$$ norm local sparse representation classification algorithm is proposed. This algorithm uses the minimum $$l_{2}$$ norm method to select the local dictionary. Then the minimum $$l_{1}$$ norm is used in the dictionary to solve sparse coefficients for classify them, and the algorithm is used to verify the gesture recognition on the constructed gesture database. The experimental results show that the algorithm can effectively reduce the calculation time while ensuring the recognition rate, and the performance of the algorithm is slightly better than KNN-SRC algorithm.

Manifold sparsity preserving projection for face and palmprint recognition

Sparsity Preserving Projection (SPP) has been recently successfully applied on pattern recognition applications and is the basis for a series of follow up extensions. However, being unsupervised for dimensionality reduction, SPP does not employ the discriminative information of class labels when projecting data into a smaller subspace. This paper proposes a manifold sparsity learning method called Manifold Sparsity Preserving Projection (MSPP) for the face and palmprint recognition. Our method employs the manifold structure for better preserving the sparsity of data in the embedding space. Differing from recent localized sparsity learning methods such as Local Sparse Representation Projections (LSRP) and Local Sparse Preserving Projections (LSPP), which enforce a one-to-one matching between a sample and its sparsely reconstructed model, our method employs manifold data structure to ensure that a sample and all its classmate’s sparsely reconstructed models remain as close as possible in the new space. We show that when manifold and sparsity information are simultaneously accounted for, their discriminative power is significantly leveraged. An immediate bonus of our approach is that there is no tuning parameter whatsoever for performance variation. We analytically demonstrate the aforementioned features of our approach and then, using a series of experiments on ORL, Yale, IIT Delhi near infrared facial, and NIR set of PolyU Multispectral palmprint databases its pragmatic consequences will be pictured.

Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models

Object tracking is a challenging task in many computer vision applications due to occlusion, scale variation and background clutter, etc. In this paper, we propose a tracking algorithm by combining discriminative global and generative multi-scale local models. In the global model, we teach a classifier with sparse discriminative features to separate the target object from the background based on holistic templates. In the multi-scale local model, the object is represented by multi-scale local sparse representation histograms, which exploit the complementary partial and spatial information of an object across different scales. Finally, a collaborative similarity score of one candidate target is input into a Bayesian inference framework to estimate the target state sequentially during tracking. Experimental results on the various challenging video sequences show that the proposed method performs favorably compared to several state-of-the-art trackers.