Texture image analysis and texture classification methods - A review

TL;DR

This review paper comprehensively discusses various texture analysis methods, emphasizing combinational approaches, their advantages, disadvantages, and performance in classification tasks, along with datasets and classifiers used in the field.

Contribution

It provides a detailed review of well-known combinational texture analysis methods, highlighting their performance, challenges, and the role of classifiers and datasets in texture classification.

Findings

Combinational methods enhance texture analysis performance.

Statistical, structural, model-based, and transform-based methods each have unique advantages.

Challenges include noise, rotation, and computational complexity.

Abstract

Tactile texture refers to the tangible feel of a surface and visual texture refers to see the shape or contents of the image. In the image processing, the texture can be defined as a function of spatial variation of the brightness intensity of the pixels. Texture is the main term used to define objects or concepts of a given image. Texture analysis plays an important role in computer vision cases such as object recognition, surface defect detection, pattern recognition, medical image analysis, etc. Since now many approaches have been proposed to describe texture images accurately. Texture analysis methods usually are classified into four categories: statistical methods, structural, model-based and transform-based methods. This paper discusses the various methods used for texture or analysis in details. New researches shows the power of combinational methods for texture analysis, which can't be in specific category. This paper provides a review on well known combinational methods in a specific section with details. This paper counts advantages and disadvantages of well-known texture image descriptors in the result part. Main focus in all of the survived methods is on discrimination performance, computational complexity and resistance to challenges such as noise, rotation, etc. A brief review is also made on the common classifiers used for texture image classification. Also, a survey on texture image benchmark datasets is included.