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Ministerio de Ciencia e Innovaci贸n (Espanya)
Generalitat de Catalunya. Ag猫ncia de Gesti贸 d鈥橝juts Universitaris i de Recerca |
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Vila Duran, Marius
Bardera i Reig, Antoni Feixas Feixas, Miquel Sbert, Mateu |
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| 2018 June 5 | |
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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鈥揕eibler distance, the difference between entropy and conditional entropy, and the Jensen鈥揟sallis 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 This work has been funded in part with Grant Numbers TIN2010-21089-C03-01 from the Spanish Government and 2009-SGR-643 from the Catalan Government. |
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| http://hdl.handle.net/2072/320779 | |
| eng | |
| MDPI (Multidisciplinary Digital Publishing Institute) | |
| Attribution 3.0 Spain | |
| http://creativecommons.org/licenses/by/3.0/es/ | |
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Informaci贸, Teoria de la
Information theory Classificaci贸 Classification |
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| Tsallis Mutual Information for Document Classification | |
| info:eu-repo/semantics/article | |
| Recercat |
