# Texture image analysis and texture classification methods - A review

arXiv: https://arxiv.org/abs/1904.06554 · HTML: https://mlsanity.com/paper/1904.06554/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2205.11083/) (2022)
- [LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision](https://mlsanity.com/paper/2405.14331/) (2024)
- [A Study on the Generality of Neural Network Structures for Monocular Depth Estimation](https://mlsanity.com/paper/2301.03169/) (2023)
- [Texture features in medical image analysis: a survey](https://mlsanity.com/paper/2208.02046/) (2022)
- [Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition](https://mlsanity.com/paper/2503.13763/) (2025)
- [Vegetation Mapping by UAV Visible Imagery and Machine Learning](https://mlsanity.com/paper/2205.11061/) (2022)
- [Applications of Machine Learning in Detecting Afghan Fake Banknotes](https://mlsanity.com/paper/2305.14745/) (2023)
- [Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging](https://mlsanity.com/paper/2402.17482/) (2024)
- [Tensor-to-Tensor Models with Fast Iterated Sum Features](https://mlsanity.com/paper/2506.06041/) (2025)
- [An Empirical Study on the Robustness of YOLO Models for Underwater Object Detection](https://mlsanity.com/paper/2509.17561/) (2025)

## Abstract (arXiv)

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.