Improving retrieval framework using information gain models |
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Authors: | Huu Ton Le Thierry Urruty Syntyche Gbèhounou François Lecellier Jean Martinet Christine Fernandez-Maloigne |
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Affiliation: | 1.ICTLab Research Laboratory,University of Science and Technology of Hanoi,Hano?,Vietnam;2.XLIM, UMR CNRS 7252,University of Poitiers,Poitiers,France;3.CNRS, Centrale Lille, UMR 9189, CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, IRCICA,Univ. Lille,Lille,France |
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Abstract: | Content-based image retrieval systems are meant to retrieve the most similar images of a collection to a query image. One of the most well-known models widely applied for this task is the bag of visual words (BoVW) model. In this paper, we introduce a study of different information gain models used for the construction of a visual vocabulary. In the proposed framework, information gain models are used as a discriminative information to index image features and select the ones that have the highest information gain values. We introduce some extensions to further improve the performance of the proposed framework: mixing different vocabularies and extending the BoVW to bag of visual phrases. Exhaustive experiments show the interest of information gain models on our retrieval framework. |
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