  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.

Data Analysis, Statistics and Probability;Artificial Intelligence;Computer Vision and Pattern Recognition;Computational Physics