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

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

2012-01-15

arxiv.org/abs/1201.3116v1

International Journal of Modern Physics C, Volume: 22, Issue: 9(2011) pp. 929-952

Computer Science

Computer Vision and Pattern Recognition

Scientific paper

10.1142/S0129183111016701

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.

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