Tsallis mutual information for document classification (Q400940)

Summary: Mutual information is one of the mostly used measures for evaluating image~similarity. In this paper, we investigate the application of three different Tsallis-based~generalizations of mutual information to analyze the similarity between scanned documents.~These three generalizations derive from the Kullback-Leibler distance, the difference~between entropy and conditional entropy, and the Jensen-Tsallis divergence, respectively.~In addition, the ratio between these measures and the Tsallis joint entropy is analyzed. The~performance of all these measures is studied for different entropic indexes in the context of document classification and registration.