Color Texture Analysis: A Survey

Abstract

In the field of image processing, texture features and color are fundamental visual cue with complementary roles. They are used in many applications and in a large variety of areas such as quality control, contentbased image retrieval, remote sensing, industrial inspection, surface inspection, object recognition, and medical image analysis. For this purpose, a large number of algorithms have been proposed for texture feature extraction. Some of them are dedicated to gray-scale images while others aim at processing both color and texture. It has been shown that, for many cases, the use of color improves the performance of gray level texture classification. This paper provides a comprehensive survey of the texture feature extraction methods that consider both texture and color information. We propose a categorization of these methods into seven classes, two of them being very recent. For each method, we present the concept, the advantages and drawbacks, and we give examples of application.

Deep Learning and Entropy-Based Texture Features for Color Image Classification

“…Despite all the texture extraction methods that have been developed, far less have been proposed for color RGB images [ 19 , 20 , 21 ]. Moreover, very few of them are based on entropy measures [ 22 ].…”

“…In this paper, we propose to study texture for color images through two approaches: the first one is a hand-crafted feature extraction method; the second one uses the deep learning approach. For the hand-crafted method, we focus on entropy-based measures because of their novelty [ 19 ] and the promising results they have shown for grayscale images. Moreover, extensions of these measures to color images have not been studied thoroughly yet.…”