Information theory techniques for multimedia data classification and retrieval

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We are in the information age where most data is stored in digital format. Thus, the management of digital documents and videos requires the development of efficient techniques for automatic analysis. Among them, capturing the similarity or dissimilarity between different document images or video frames are extremely important. In this thesis, we first analyze for several image resolutions the behavior of three different families of image-based similarity measures applied to invoice classification. In these three set of measures, the computation of the similarity between two images is based, respectively, on intensity differences, mutual information, and normalized compression distance. As the best results are obtained with mutual information-based measures, we proceed to investigate the application of three different Tsallis-based generalizations of mutual information for different entropic indexes. These three generalizations derive respectively from the Kullback-Leibler distance, the difference between entropy and conditional entropy, and the Jensen-Shannon divergence. In relation to digital video processing, we propose two different information-theoretic approaches based, respectively, on Tsallis mutual information and Jensen-Tsallis divergence to detect the abrupt shot boundaries of a video sequence and to select the most representative keyframe of each shot. Finally, Shannon entropy has been commonly used to quantify the image informativeness. The main drawback of this measure is that it does not take into account the spatial distribution of pixels. In this thesis, we analyze four information-theoretic measures that overcome this limitation. Three of them (entropy rate, excess entropy, and erasure entropy) consider the image as a stationary stochastic process, while the fourth (partitional information) is based on an information channel between image regions and histogram bins ​
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