TEXTURE IMAGE ANALYSIS USING LBP AND DATA COMPRESSION

Nuo Zhang, Toshinori Watanabe

2012

Abstract

Texture classification is an important technology widely applied in many application fields in image processing. In this study, a novel representation method for texture image is proposed. The proposed approach is based on the consideration of using data compression to search the essential feature of frequent pattern in texture images. Furthermore, to deal with the difficult situation caused by different situations of photography, local binary pattern (LBP) is introduced to the proposed approach to reduce the numbers of varieties of patterns in texture image. Compresibility vector space is adopted in this study instead of learning phase. Based on the patterns extracted by LBP operator which are invariant to monotonic gray-level transformations, data compression helps extract the longest and frequent features. These features provide high analytical ability for texture image. The simulation results will show good performance of our approach.

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Paper Citation


in Harvard Style

Zhang N. and Watanabe T. (2012). TEXTURE IMAGE ANALYSIS USING LBP AND DATA COMPRESSION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 437-440. DOI: 10.5220/0003850904370440

in Bibtex Style

@conference{visapp12,
author={Nuo Zhang and Toshinori Watanabe},
title={TEXTURE IMAGE ANALYSIS USING LBP AND DATA COMPRESSION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={437-440},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003850904370440},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - TEXTURE IMAGE ANALYSIS USING LBP AND DATA COMPRESSION
SN - 978-989-8565-03-7
AU - Zhang N.
AU - Watanabe T.
PY - 2012
SP - 437
EP - 440
DO - 10.5220/0003850904370440