Discriminative Sparse Representation Research Articles

Artifact suppressed dictionary learning for low-dose CT image processing.

Low-dose computed tomography (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called "artifact suppressed dictionary learning (ASDL)." In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation operation based on these dictionaries. Then, a general dictionary learning processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems.

Hyperspectral Image Classification via Discriminative Sparse Representation with Extended LBP Texture

Hyperspectral images (HSI) have rich texture information, so combining texture information and image spectral information can improve the recognition accuracy. Sparse representation has significant success in image classification. In this paper, we propose a new discriminative sparse-based classification framework using spectral data and extended Local Binary Patterns (LBP) texture. Firstly, we propose an extended LBP coding for HSI classification. Then we formulate an optimization problem that combines the objective function of classification with the representation error by sparsity. Furthermore, we use a procedure similar to K-SVD algorithm to learn the discriminative dictionary. The experimental results show that the proposed discriminative spasity-based classification of image including the extended LBP texture outperforms the classical HSI classification algorithms.

Robust tracking via discriminative sparse feature selection

In this paper, we propose a novel generative tracking approach based on discriminative sparse feature selection. The sparse features are the discriminative sparse representation of samples, which are achieved by learning a compact and discriminative dictionary. Besides the target templates, the proposed approach also incorporates the close-background templates to approximate the partial variations. We learn the dictionary and a classifier together, and search the tracking result with the maximum similarity and the minimal reconstruction error criterion using the discrimination of sparse features. In addition, we resample the close-background templates and update the dictionary in an adaptive way during tracking. Experimental results on several challenging video sequences demonstrate that the proposed approach has more favorable performance than the state-of-the-art approaches.

Monocular human motion tracking with discriminative sparse representation

In this work, we address the problem of monocular tracking the human motion based on the discriminative sparse representation. The proposed method jointly trains the dictionary and the discriminative linear classifier to separate the human being from the background. We show that using the online dictionary learning, the tracking algorithm can adapt the variation of human appearance and background environment. We compared the proposed method with four state-of-the-art tracking algorithms on eight benchmark video clips (Faceocc, Sylv, David, Singer, Girl, Ballet, OneLeaveShopReenter2cor, and ThreePastShop2cor). Qualitative and quantitative experimental validation results are discussed at length. The proposed algorithm for human tracking achieves superior tracking results, and a Matlab run time on a standard desktop machine of four frames per second.

Maximum margin sparse representation discriminative mapping with application to face recognition

Sparse subspace learning has drawn more and more attention recently. We propose a novel sparse subspace learning algorithm called maximum margin sparse representation discriminative mapping (MSRDM), which adds the discriminative information into sparse neighborhood preservation. Based on combination of maximum margin discriminant criterion and sparse representation, MSRDM can preserve both local geometry structure and classification information. MSRDM can avoid the small sample size problem in face recognition naturally and the computation is efficient. To improve face recognition performance, we propose to integrate Gabor-like complex wavelet and natural image features by complex vectors as input features of MSRDM. Experimental results on ORL, UMIST, Yale, and PIE face databases demonstrate the effectiveness of the proposed face recognition method.

Human Behavior Recognition Using Structured and Discriminative Sparse Representation

Human Behavior Recognition Using Structured and Discriminative Sparse Representation

Learning Discriminative Sparse Representations for Modeling, Source Separation, and Mapping of Hyperspectral Imagery

A method is presented for subpixel modeling, mapping, and classification in hyperspectral imagery using learned block-structured discriminative dictionaries, where each block is adapted and optimized to represent a material in a compact and sparse manner. The spectral pixels are modeled by linear combinations of subspaces defined by the learned dictionary atoms, allowing for linear mixture analysis. This model provides flexibility in source representation and selection, thus accounting for spectral variability, small-magnitude errors, and noise. A spatial-spectral coherence regularizer in the optimization allows pixel classification to be influenced by similar neighbors. We extend the proposed approach for cases for which there is no knowledge of the materials in the scene, unsupervised classification, and provide experiments and comparisons with simulated and real data. We also present results when the data have been significantly undersampled and then reconstructed, still retaining high-performance classification, showing the potential role of compressive sensing and sparse modeling techniques in efficient acquisition/transmission missions for hyperspectral imagery.