Enhancing Volumetric Bouligand-Minkowski Fractal Descriptors by using Functional Data Analysis

TL;DR

This paper introduces a Functional Data Analysis transform to improve volumetric Bouligand-Minkowski fractal descriptors, enhancing texture classification accuracy by transforming descriptors into a functional data space.

Contribution

It presents a novel FDA-based transform for fractal descriptors, improving their performance in texture classification tasks.

Findings

Enhanced classification accuracy with FDA-transformed descriptors

Significant improvement over original descriptors in experiments

Validated on well-known texture datasets

Abstract

This work proposes and study the concept of Functional Data Analysis transform, applying it to the performance improving of volumetric Bouligand-Minkowski fractal descriptors. The proposed transform consists essentially in changing the descriptors originally defined in the space of the calculus of fractal dimension into the space of coefficients used in the functional data representation of these descriptors. The transformed decriptors are used here in texture classification problems. The enhancement provided by the FDA transform is measured by comparing the transformed to the original descriptors in terms of the correctness rate in the classification of well known datasets.