Enhancing Volumetric Bouligand-Minkowski Fractal Descriptors by using Functional Data Analysis
This work addresses texture classification problems, but it is incremental as it builds on existing fractal descriptors with a specific transform.
The authors tackled the problem of improving volumetric Bouligand-Minkowski fractal descriptors for texture classification by applying a Functional Data Analysis transform, resulting in enhanced correctness rates as measured on well-known datasets.
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.