Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

作     者：Kalakech, Mariam Porebski, Alice Vandenbroucke, Nicolas Hamad, Denis 

作者机构：Lebanese Univ Fac Econ & Business Adm Branch 1 Beirut 21219 Lebanon Univ Littoral Opal Coast LISIC Lab F-62228 Calais France 

出 版 物：《JOURNAL OF IMAGING》 (J. Imaging)

年 卷 期：2018年第4卷第10期

页      面：112-112页

核心收录：

基　　金：This research received no external funding 

主　　题：histogram selection local binary pattern unsupervised selection score color texture 

摘      要：These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.