# Supervised Texture Segmentation: A Comparative Study

arXiv: https://arxiv.org/abs/1601.00212 · HTML: https://mlsanity.com/paper/1601.00212/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2016.

## Built on by (most cited first, 2 of 2)

- [An Ensemble Model with Attention Based Mechanism for Image Captioning](https://mlsanity.com/paper/2501.14828/) (2025)
- [Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation](https://mlsanity.com/paper/2506.05399/) (2025)

## Abstract (arXiv)

This paper aims to compare between four different types of feature extraction approaches in terms of texture segmentation. The feature extraction methods that were used for segmentation are Gabor filters (GF), Gaussian Markov random fields (GMRF), run-length matrix (RLM) and co-occurrence matrix (GLCM). It was shown that the GF performed best in terms of quality of segmentation while the GLCM localises the texture boundaries better as compared to the other methods.