Multi-color space local binary pattern-based feature selection for texture classification

Combination of LBP Bin and Histogram Selections for Color Texture Classification

“…In the framework of color texture classification, many authors try to determine the “best” color space in order to improve the results of their proposed classification approach. However, it was shown that it is difficult to a priori determine the best color space suited to all applications of color texture classification [ 16 ]. For this reason, an alternative approach emerged: it consists of simultaneously exploiting the properties of several color spaces.…”

“…The Multi Color Space Histogram Selection (MCSHS) approach analyzes LBP histograms computed from texture images coded into several color spaces [ 16 ]. Indeed, rather than looking for the best color space, these approaches first compute LBP histograms from several color spaces and then selects, out of the different candidate LBP histograms, those which are the most discriminant for the considered application in a supervised context.…”

“…This approach, which proposes to learn the most reliable and robust dominant bins by considering intra-class similarity and inter-class dissimilarity, is very interesting since it outperforms Ojala’s uniform LBP operator and Liao’s DLBP in the experiments on three texture databases. It has also been recently extended to the multi color space domain [ 16 ].…”

“…Several scores were proposed in the literature to evaluate the relevance of histograms: the Intra-Class Similarity score (ICS-score), proposed by Porebski et al [ 17 ], which is based on an intra-class similarity measure; the Adapted Supervised Laplacian score (ASL-score) and Adapted Laplacian score (AL-score), proposed by Kalakech et al [ 18 , 19 ], which evaluates the relevance of the histograms using the local properties of the image data; the Simba-2 score, proposed by Mouhajid et al [ 20 ], which is based on the hypothesis margin and the distance; and the Sparse Adapted Supervised Laplacian score (SpASL-score) [ 21 ], which is based on the ASL-score and a sparse representation. The LBP histogram selection approach using ICS or ASL scores was recently extended to the multi color space domain and showed its relevance compared to the bin selection approach proposed by Guo [ 16 ].…”

“…For this purpose, the sparsity score proposed by Liu [ 22 ] is used for selecting the most discriminant bins of LBP histograms extracted from images coded in several color spaces, leading to the Sparse Multi Color Space Bin Selection (Sparse-MCSBS) approach. This second minor contribution corresponds to the MCSBS approach proposed in [ 16 ] that was extended to a sparse representation. These two approaches, Sparse-MCSHS and Sparse-MCSBS, are then combined to form the Sparse-MCSHBS approach (Sparse Multi Color Space Histogram and Bin Selection).…”