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.

Automatic shot boundary detection and keyframe selection constitute major goals in video processing. We propose two different information-theoretic approaches to detect the abrupt shot boundaries of a video sequence. These approaches are, respectively, based on two information measures, Tsallis mutual information and Jensen-Tsallis divergence, that are used to quantify the similarity between two frames. Both measures are also used to find out the most representative keyframe of each shot. The representativeness of a frame is basically given by its average similarity with respect to the other frames of the shot. Several experiments analyze the behavior of the proposed measures for different color spaces (RGB, HSV, and Lab), regular binnings, and entropic indices. In particular, the Tsallis mutual information for the HSV and Lab color spaces with only 8 regular bins for each color component and an entropic index between 1. 5 and 1. 8 substantially improve the performance of previously proposed methods based on mutual information and Jensen-Shannon divergenceThis work has been funded in part by grants from the Spanish Government (Nr. TIN2010-21089-C03-01), from the Catalan Government (Nr. 2009-SGR-643 and Nr. 2010-CONE2-00053), and from the Natural Science Foundation of China (61179067, 61103005, 60879003

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