Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

Gao, Shenghua; Tsang, Ivor W.; Chia, Liang-Tien

doi:10.1109/tpami.2012.63

Cited by 338 publications

(196 citation statements)

References 26 publications

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“…In addition, in conformity with previous work [13], the Laplacian regularized Lasso is also solved by a modified feature sign search (FSS) method. In this paper, we try to present a fair comparison among all priors.…”

Section: B Models and Methodsmentioning

confidence: 81%

“…Recently, structured priors have been incorporated into HSI classification [7], which can be sorted into three categories. (a) Priors that only exploit the correlations and dependencies among the neighboring spectral pixels or their sparse coefficient vectors, which includes joint sparsity [12], graph regularized Lasso (referred as the Laplacian regularized Lasso) [13] and the low-rank Lasso [14]. (b) Priors that only exploit the inherent structure of the dictionary, such as group Lasso [15].…”

Section: Introductionmentioning

confidence: 99%

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Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

Sun

Nasrabadi

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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Abstract-Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results compared with that of traditional classifiers such as the support vector machine (SVM). Recently, by incorporating additional structured sparsity priors, the second generation SRCs have appeared in the literature and are reported to further improve the performance of HSI. These priors are based on exploiting the spatial dependencies between the neighboring pixels, the inherent structure of the dictionary, or both. In this paper, we review and compare several structured priors for sparserepresentation-based HSI classification. We also propose a new structured prior called the low rank group prior, which can be considered as a modification of the low rank prior. Furthermore, we will investigate how different structured priors improve the result for the HSI classification.

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Section: B Models and Methodsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

Sun

Nasrabadi

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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show abstract

“…Based on the fact that pixels within a local patch (where the center pixel is the target pixel to be processed) have a high probability of being associated with the same thematic class, the sparse representation coefficients of these pixels are also expected to be similar. Therefore, the spatial pooling operation can be considered a reasonable way to merge these similar sparse representation coefficient vectors and yield a new feature vector with better discriminative ability, which is an approach that has been widely used in the literature [33,34].…”

Section: Incorporating Spatial Information With the Spatial Max Poolimentioning

confidence: 99%

Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation

Zhai

Zhang

et al. 2017

Remote Sensing

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Hyperspectral image (HSI) clustering is generally a challenging task because of the complex spectral-spatial structure. Based on the assumption that all the pixels are sampled from the union of subspaces, recent works have introduced a robust technique-the sparse subspace clustering (SSC) algorithm and its enhanced versions (SSC models incorporating spatial information)-to cluster HSIs, achieving excellent performances. However, these methods are all based on the linear representation model, which conflicts with the well-known nonlinear structure of HSIs and limits their performance to a large degree. In this paper, to overcome these obstacles, we present a new kernel sparse subspace clustering algorithm with a spatial max pooling operation (KSSC-SMP) for hyperspectral remote sensing data interpretation. The proposed approach maps the feature points into a much higher dimensional kernel space to extend the linear sparse subspace clustering model to nonlinear manifolds, which can better fit the complex nonlinear structure of HSIs. With the help of the kernel sparse representation, a more accurate representation coefficient matrix can be obtained. A spatial max pooling operation is utilized for the representation coefficients to generate more discriminant features by integrating the spatial-contextual information, which is essential for the accurate modeling of HSIs. This paper is an extension of our previous conference paper, and a number of enhancements are put forward. The proposed algorithm was evaluated on two well-known hyperspectral data sets-the Salinas image and the University of Pavia image-and the experimental results clearly indicate that the newly developed KSSC-SMP algorithm can obtain very competitive clustering results for HSIs, outperforming the current state-of-the-art clustering methods.

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“…Afterwards, by combining these features with SPM, the authors propose Kernel Sparse Representation Spatial Pyramid Matching (KSRSPM). Besides this approach, Gao et al [66] explore another sparse codingbased approach (LScSPM) by considering the instable sparse code produced by different sparse coding techniques [60,62]. The authors use Laplacian sparse coding framework to address this issue.…”

Section: Literature Reviewmentioning

confidence: 99%

DTCTH: a discriminative local pattern descriptor for image classification

Rahman

et al. 2017

J Image Video Proc.

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Despite lots of effort being exerted in designing feature descriptors, it is still challenging to find generalized feature descriptors, with acceptable discrimination ability, which are able to capture prominent features in various image processing applications. To address this issue, we propose a computationally feasible discriminative ternary census transform histogram (DTCTH) for image representation which uses dynamic thresholds to perceive the key properties of a feature descriptor. The code produced by DTCTH is more stable against intensity fluctuation, and it mainly captures the discriminative structural properties of an image by suppressing unnecessary background information. Thus, DTCTH becomes more generalized to be used in different applications with reasonable accuracies. To validate the generalizability of DTCTH, we have conducted rigorous experiments on five different applications considering nine benchmark datasets. The experimental results demonstrate that DTCTH performs as high as 28.08% better than the existing state-of-the-art feature descriptors such as GIST, SIFT, HOG, LBP, CLBP, OC-LBP, LGP, LTP, LAID, and CENTRIST.

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Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

Cited by 338 publications

References 26 publications

Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation

DTCTH: a discriminative local pattern descriptor for image classification

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