Multi-q Analysis of Image Patterns
Data Analysis, Statistics and Probability
2011-12-30 v1 Artificial Intelligence
Computer Vision and Pattern Recognition
Computational Physics
Abstract
This paper studies the use of the Tsallis Entropy versus the classic Boltzmann-Gibbs-Shannon entropy for classifying image patterns. Given a database of 40 pattern classes, the goal is to determine the class of a given image sample. Our experiments show that the Tsallis entropy encoded in a feature vector for different indices has great advantage over the Boltzmann-Gibbs-Shannon entropy for pattern classification, boosting recognition rates by a factor of 3. We discuss the reasons behind this success, shedding light on the usefulness of the Tsallis entropy.
Keywords
Cite
@article{arxiv.1112.6371,
title = {Multi-q Analysis of Image Patterns},
author = {Ricardo Fabbri and Wesley N. Gonçalves and Francisco J. P. Lopes and Odemir M. Bruno},
journal= {arXiv preprint arXiv:1112.6371},
year = {2011}
}