Multiscale Fractal Descriptors Applied to Texture Classification

TL;DR

This paper introduces a novel multiscale fractal descriptor method that combines space-scale transforms with Bouligand-Minkowski fractal descriptors for improved gray-level texture classification, demonstrating superior accuracy and efficiency.

Contribution

The work presents a new approach integrating multiscale transforms with fractal descriptors, enhancing noise robustness and classification success in texture analysis.

Findings

Higher classification success rate on Brodatz dataset

Reduced number of descriptors needed for accurate classification

Effective noise attenuation in texture descriptors

Abstract

This work proposes the combination of multiscale transform with fractal descriptors employed in the classification of gray-level texture images. We apply the space-scale transform (derivative + Gaussian filter) over the Bouligand-Minkowski fractal descriptors, followed by a threshold over the filter response, aiming at attenuating noise effects caused by the final part of this response. The method is tested in the classification of a well-known data set (Brodatz) and compared with other classical texture descriptor techniques. The results demonstrate the advantage of the proposed approach, achieving a higher success rate with a reduced amount of descriptors.