Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis

TL;DR

This paper introduces a novel multiscale Fourier-based fractal descriptor method for color texture analysis, improving classification accuracy by nearly 3% over existing techniques.

Contribution

It develops a new two-step approach combining color space transformation and multiscale Fourier fractal analysis for enhanced color texture discrimination.

Findings

Achieved nearly 3% higher classification accuracy than state-of-the-art methods.

Validated effectiveness on two color texture datasets.

Demonstrated the method's superiority in texture discrimination.

Abstract

The present work proposes the development of a novel method to provide descriptors for colored texture images. The method consists in two steps. In the first, we apply a linear transform in the color space of the image aiming at highlighting spatial structuring relations among the color of pixels. In a second moment, we apply a multiscale approach to the calculus of fractal dimension based on Fourier transform. From this multiscale operation, we extract the descriptors used to discriminate the texture represented in digital images. The accuracy of the method is verified in the classification of two color texture datasets, by comparing the performance of the proposed technique to other classical and state-of-the-art methods for color texture analysis. The results showed an advantage of almost 3% of the proposed technique over the second best approach.