Using Visual Concepts and Fast Visual Diversity to Improve Image Retrieval

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Sabrina Tollari, Macrin Detyniecki, Ali-Fakeri Tabrizi, Christophe Marsala, Massih-Reza Amini, Patrick Gallinari
Laboratoire d'Informatique Paris 6
case 169
104, Av. Président Kennedy
75016 Paris, Francee

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In this article, we focus our efforts (i) on the study of how to automatically extract and exploit visual concepts and (ii) on fast visual diversity. First, in the Visual Concept Detection Task (VCDT), we look at the mutual exclusion and implication relations between VCDT concepts in order to improve the automatic image annotation by Forest of Fuzzy Decision Trees (FFDTs). Second, in the ImageCLEFphoto task, we use the FFDTs learn in VCDT task and WordNet to improve image retrieval. Third, we apply a fast visual diversity method based on space clustering to improve the cluster recall score. This study shows that there is a clear improvement, in terms of precision or cluster recall at 20, when using the visual concepts explicitly appearing in the query and that space clustering can be efficiently used to improve cluster recall.