Combination of LBP Bin and Histogram Selections for Color Texture Classification - Archive ouverte HAL Access content directly
Journal Articles Journal of Imaging Year : 2020

Combination of LBP Bin and Histogram Selections for Color Texture Classification

Abstract

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.

Dates and versions

hal-03031173 , version 1 (30-11-2020)

Identifiers

Cite

Alice Porebski, Vinh Truong Hoang, Nicolas Vandenbroucke, Denis Hamad. Combination of LBP Bin and Histogram Selections for Color Texture Classification. Journal of Imaging, 2020, 6 (6), pp.53. ⟨10.3390/jimaging6060053⟩. ⟨hal-03031173⟩
25 View
0 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More