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Local structure preserving sparse coding for infrared target recognition

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for... Full description

Journal Title: PLoS ONE March 2017, Vol.12(3)
Main Author: Han, Jing
Other Authors: Yue, Jiang , Zhang, Yi , Bai, Lianfa
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0173613
Link: http://search.proquest.com/docview/1888969745/?pq-origsite=primo
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title: Local structure preserving sparse coding for infrared target recognition
format: Article
creator:
  • Han, Jing
  • Yue, Jiang
  • Zhang, Yi
  • Bai, Lianfa
subjects:
  • Classification
  • Occlusion
  • Algorithms
  • Kernels
  • Interference
  • Imaging
  • Imaging
ispartof: PLoS ONE, March 2017, Vol.12(3)
description: Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc-...
language: eng
source:
identifier: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0173613
fulltext: fulltext
issn:
  • 19326203
  • 1932-6203
url: Link


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descriptionSparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc-...
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abstractSparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc-...
doi10.1371/journal.pone.0173613
urlhttp://search.proquest.com/docview/1888969745/
pagese0173613
date2017-03-01