Multi-q Analysis of Image Patterns

Details Multi-q Analysis of Image Patterns Multi-q Analysis of Image Patterns

2011-12-29

arxiv.org/abs/1112.6371v1

Physics

Data Analysis, Statistics and Probability

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Multi-q Analysis of Image Patterns does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Multi-q Analysis of Image Patterns, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Multi-q Analysis of Image Patterns will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-728696