Narrow Results

This paper investigates how to boost region-based image segmentation by inheriting the advantages of sparse representation and low-rank representation. A novel image segmentation model, called nonconvex regularization based sparse and low-rank coupling model, is presented for such a purpose. We aim at finding the optimal solution which is provided with sparse and low-rank simultaneously. This is achieved by relaxing sparse representation problem as L_1/2 norm minimization other than the L₁ norm minimization, while relaxing low-rank representation problem as the S_1/2 norm minimization other than the nuclear norm minimization. This coupled model can be solved efficiently through the Augmented Lagrange Multiplier (ALM) method and half-threshold operator. Compared to the other state-of-the-art methods, the new method is better at capturing the global structure of the whole data, the robustness is better and the segmentation accuracy is also competitive. Experiments on two public image segmentation databases well validate the superiority of our method.

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten $p$-norm and ${\ell }_p$-norm, instead of the nuclear norm and ${\ell }_1$-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.

Subspace clustering analysis algorithms are often employed when dealing with high-dimensional data. As a representative approach, Low-Rank Representation (LRR) of data has achieved great success for subspace segmentation tasks in applications such as image processing. The traditional LRR-related methods consist of two separate tasks: first, the affinity graph construction by using lowrank minimization techniques, and then the spectral clustering, which is done on the affinity graph to get the final segmentation. Since these two steps are independent of each other, this method does not guarantee that the results obtained by the algorithm are globally optimal. In this paper, a method called Robust Structured Low-Rank Representation (RSLRR) is proposed, by integrating the two above mentioned tasks and solve a joint optimization problem. This paper also puts forward a method to solve the joint optimization problem, which can efficiently get both the segmentation and the structured low-rank representation. Experiments on several standard datasets show that, compared with other algorithms, the algorithm proposed in this paper can achieve better clustering results.

Classification of epileptic electroencephalograms can provide strong technical support for early warning and monitoring of epilepsy. However, Electroencephalogram (EEG) has characteristics of specificity, multi-feature, and susceptibility to noise interference. Therefore, this paper proposes a joint low-rank representation and transfer multi-view learning (LRR-TML) approach for EEG epilepsy classification. The LRR-TML approach inherits the advantages of low-rank representation, filters redundant features and noise information, and focuses on the main feature and significant feature of EEG. The LRR-TML approach introduces the ideas of transfer learning and multi-view learning on the framework of low-rank representation, mines the linear correlation between different domains in the subspace through low-rank representation, and effectively transfers the discriminative knowledge of the source domain to the target domain. According to the principles of complementarity and consistency of views, the LRR-TML approach comprehensively describes EEG by integrating various features, maximizing the consistency between different views and minimizing the divergence between views. Experimental results on the Bonn dataset show that the LRR-TML approach achieves the best classification results among all comparison approaches.

In this paper, we address the problem of removing striping noise in hyperspectral images (HSI). To this end, we develop a novel destriping method by taking advantage of the spectral and spatial information of the hyperspectral image simultaneously. To obtain satisfactory destriped results, our consideration is two-fold: (1) To model the spectral information of HSI, we utilize low-rank representation to exploit the low-rank property of HSI; (2) To incorporate the spatial information into our method, we adopt the Huber-Markov random field (HMRF) prior model to preserve the edge information of HSI while reducing the striping noise. Finally, we integrate the spectral and spatial model into a unified objective function. In addition, we also devise an effective optimization algorithm to minimize the objective function. The experimental results on real-world HSI data validate the efficacy of the proposed scheme for destriping of HSI.

For the object loss problem in the tracking process caused by illumination, occlusion, pose variation, and motion blur, the tracking method based on dual fuzzy low-rank approximation in a particle filter framework is proposed in this paper. Firstly, multiple constraint regions are built to filter insignificant samples, and more distinguished candidate samples are selected. Secondly, dual fuzzy observation function of each candidate sample is created based on the designed low-rank approximation representations of object and background. Then the generalized tracking results are obtained by computing membership degrees of dual fuzzy observation functions. Finally, based on the spatial coherency principle, the final tracking result is determined from the generalized results by measuring similarities of consecutive objects. The proposed method shows good performance as compared with several state-of-the-art trackers on challenging benchmark sequences.