Texture image analysis and texture classification methods - A review
Laleh Armi, Shervan Fekri-Ershad

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…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Currency Recognition and Detection · Advanced Image and Video Retrieval Techniques
