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Combination of LBP Bin and Histogram Selections for Color Texture Classification by Porebski, Alice; Truong Hoang, Vinh; Vandenbroucke, Nicolas; Hamad, Denis is a Computer Science article available to read on EtoBox.

What is Combination of LBP Bin and Histogram Selections for Color Texture Classification about?

LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.

Who reads Combination of LBP Bin and Histogram Selections for Color Texture Classification?

Author

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

Publisher

MDPI AG; Multidisciplinary Digital Publishing Institute; Basel: MDPI AG, 2015- (ISSN 2313-433X)

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)