Multi-q Analysis of Image Patterns

TL;DR

This paper compares Tsallis entropy and Boltzmann-Gibbs-Shannon entropy for image pattern classification, demonstrating that Tsallis entropy significantly improves recognition accuracy across 40 pattern classes.

Contribution

It introduces the use of Tsallis entropy with multiple q indices as a feature for image classification, showing its superiority over traditional entropy methods.

Findings

Tsallis entropy boosts recognition rates by a factor of 3.

Using multiple q indices enhances pattern classification performance.

Tsallis entropy provides valuable insights into image pattern recognition.

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 $q$ 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.