Unsupervised LBP histogram selection for color texture classification via sparse representation
Vinh Truong Hoang · 2018
In recent years, LBP and its variants have led to significant progress in applying texture methods to different applications. However, this operator tends to produce high dimensional feature vectors, especially when the number of considered neighboring pixels increases or when it is applied to color images. Various approaches are proposed to obtain more discriminative, robust LBP-features with reduced feature dimensionality. LBP histogram selection is a method to reduce the number of histogram to characterize color image. In this paper, we propose to construct sparse similarity matrix by an unsupervised way for LBP histogram selection.