Fractal Descriptors Based on Fourier Spectrum Applied to Texture Analysis

TL;DR

This paper introduces a new fractal descriptor technique for texture analysis that uses a multiscale transform on the Fourier spectrum, improving classification accuracy over existing methods.

Contribution

The paper presents a novel fractal descriptor based on a multiscale transform of the Fourier spectrum for enhanced texture classification.

Findings

The proposed descriptors outperform existing fractal descriptors in classification accuracy.

The method effectively captures multiscale texture features.

Validation shows improved precision in texture classification tasks.

Abstract

This work proposes the development and study of a novel technique for the generation of fractal descriptors used in texture analysis. The novel descriptors are obtained from a multiscale transform applied to the Fourier technique of fractal dimension calculus. The power spectrum of the Fourier transform of the image is plotted against the frequency in a log- log scale and a multiscale transform is applied to this curve. The obtained values are taken as the fractal descriptors of the image. The validation of the propose is performed by the use of the descriptors for the classification of a dataset of texture images whose real classes are previously known. The classification precision is compared to other fractal descriptors known in the literature. The results confirm the efficiency of the proposed method.