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Communication Dans Un Congrès Année : 2017

LBP Histogram Selection based on Sparse Representation for Color Texture Classification

Résumé

In computer vision fields, LBP histogram selection techniques are mainly applied to reduce the dimension of color texture space in order to increase the classification performances. This paper proposes a new histogram selection score based on Jeffrey distance and sparse similarity matrix obtained by sparse representation. Experimental results on three benchmark texture databases show that the proposed method improves the performance of color texture classification represented in different color spaces.

Dates et versions

hal-03032041 , version 1 (30-11-2020)

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Vinh Truong Hoang, Alice Porebski, Nicolas Vandenbroucke, Denis Hamad. LBP Histogram Selection based on Sparse Representation for Color Texture Classification. VISAPP 2017 : 12th International Conference on Computer Vision Theory and Applications, Feb 2017, Porto, Portugal. pp.476-483, ⟨10.5220/0006128204760483⟩. ⟨hal-03032041⟩
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