An Enhanced Local Binary Pattern for Texture Classification

DOI

10.2991/msota-16.2016.93How to use a DOI?

Keywords

Completed Local binary Pattern (CLBP); Border/Interior Pixel Classification (BIC); Texture Classification

Abstract

In order to improve the recognition rate of texture classification, an enhanced Local binary pattern, called completed local binary pattern based on gray level and structural information (CLBP_GLSI), is proposed in this paper. We firstly proposed an structural texture operator called gray level and structural information (GLSI), which adopts the average gray level of image to make image converted into binary images, and binary images are encoded as border or interior pixels image by Border/Interior Pixel Classification (BIC). Secondly, by combing with CLBP_M, CLBP_S and GLSI in into joint or hybrid distributions, the CLBP_GLSI are obtained. Experimental results obtained from two databases show that CLBP_GLSI achieves better results than other texture features.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Huawei Tao
AU  - Rugang Wang
AU  - Li Zhao
PY  - 2016/12
DA  - 2016/12
TI  - An Enhanced Local Binary Pattern for Texture Classification
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 421
EP  - 424
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.93
DO  - 10.2991/msota-16.2016.93
ID  - Tao2016/12
ER  -