Combination of LBP Bin and Histogram Selections for Color Texture Classification.

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

doi:10.3390/jimaging6060053

Alice Porebski, Vinh Truong Hoang + Show 2 more

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https://doi.org/10.3390/jimaging6060053

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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.

Highlights

Texture analysis is one of the major topics in the field of computer vision and has many important applications including face recognition, object detection, image filtering, segmentation, and content-based access to image databases [1]
That is why we propose introducing the combination of Local Binary Pattern (LBP) bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure
We show that the combination of bin and histogram selections outperforms the simple Multi Color Space Histogram Selection (MCSHS) and Multi Color Space Bin Selection (MCSBS) approaches: an average improvement of 0.8% compared to the MCSHS strategy and 3.1% compared to the MCSBS approach is observed

Summary

Introduction

Texture analysis is one of the major topics in the field of computer vision and has many important applications including face recognition, object detection, image filtering, segmentation, and content-based access to image databases [1]. Texture classification can be defined as a task that assigns a texture into one of a set of predefined categories. This step requires an efficient descriptor in order to represent and discriminate the different texture classes. Texture analysis was extensively studied and a wide variety of texture representations were proposed [2]. Among these approaches, the Local Binary Pattern (LBP) descriptor proposed by Ojala et al is known as one of the most successful statistical approaches due to its efficiency, robustness against illumination intensity changes, and relative fast calculation [3]. Considering a particular color space, the LBP descriptor is applied on each color component independently or on pairs of color components jointly, leading to several LBP histograms for characterizing a color texture

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