LBP Histogram Selection based on Sparse Representation for Color Texture Classification

Vinh Truong Hoang, Alice Porebski, Nicolas Vandenbroucke, Denis Hamad

Abstract

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.

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Paper Citation


in Harvard Style

Hoang V., Porebski A., Vandenbroucke N. and Hamad D. (2017). LBP Histogram Selection based on Sparse Representation for Color Texture Classification . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 476-483. DOI: 10.5220/0006128204760483


in Bibtex Style

@conference{visapp17,
author={Vinh Truong Hoang and Alice Porebski and Nicolas Vandenbroucke and Denis Hamad},
title={LBP Histogram Selection based on Sparse Representation for Color Texture Classification},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={476-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006128204760483},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - LBP Histogram Selection based on Sparse Representation for Color Texture Classification
SN - 978-989-758-225-7
AU - Hoang V.
AU - Porebski A.
AU - Vandenbroucke N.
AU - Hamad D.
PY - 2017
SP - 476
EP - 483
DO - 10.5220/0006128204760483