Multi-q Analysis of Image Patterns
  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. 
Ricardo Fabbri, Wesley N. Gon\c{c}alves, Francisco J. P. Lopes, Odemir   M. Bruno
physics.data-an cs.AI cs.CV physics.comp-ph