Texture Fuzzy Segmentation using Skew Divergence Adaptive Affinity Functions

TL;DR

This paper introduces an adaptive fuzzy segmentation method that uses texture-based affinity functions with variable neighborhood sizes, improving segmentation of textured objects in images.

Contribution

It extends fuzzy segmentation by incorporating adaptive affinity functions that adjust neighborhood size based on local texture characteristics.

Findings

Effective segmentation of textured objects in mosaic images.

Outperforms recent texture segmentation algorithms.

Demonstrates applicability on Brodatz database images.

Abstract

Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been successfully used in the segmentation of images from a wide variety of sources. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully described by simple statistics computed over a very restricted area. In this paper, we propose an extension of the fuzzy segmentation algorithm that uses adaptive textural affinity functions to perform the segmentation of such objects on bidimensional images. The adaptive affinity functions compute their appropriate neighborhood size as they compute the texture descriptors surrounding the seed spels (spatial elements), according to the characteristics of the texture being processed. The algorithm then segments the image with an appropriate neighborhood for each object. We performed experiments on mosaic images that were composed using images from the Brodatz database, and compared our results with the ones produced by a recently published texture segmentation algorithm, showing the applicability of our method.