Fractal descriptors based on the probability dimension: a texture analysis and classification approach

TL;DR

This paper introduces a new multiscale fractal descriptor method based on probability dimension estimation for gray-level texture image analysis, demonstrating improved classification performance on benchmark datasets.

Contribution

It presents a novel fractal descriptor technique utilizing multiscale transforms and probability dimension for enhanced texture image analysis and classification.

Findings

Effective in texture discrimination

Outperforms existing methods on benchmark datasets

Demonstrates robustness in classification tasks

Abstract

In this work, we propose a novel technique for obtaining descriptors of gray-level texture images. The descriptors are provided by applying a multiscale transform to the fractal dimension of the image estimated through the probability (Voss) method. The effectiveness of the descriptors is verified in a classification task using benchmark over texture datasets. The results obtained demonstrate the efficiency of the proposed method as a tool for the description and discrimination of texture images.