Improving retrieval framework using information gain models

Huu Ton Le (1, 2) , Thierry Urruty (1, 2) , Syntyche Gbehounou (1, 2) , François Lecellier (1, 2) , Jean Martinet (3, 4) , Christine Fernandez-Maloigne (1, 2)

1 XLIM-ASALI - Synthèse et analyse d'images
2 UP - Université de Poitiers = University of Poitiers
3 Université de Lille, Sciences et Technologies
4 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

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.

Mots clés: Image Retrieval; Vocabulary Construction; Bags of Visual Words; Saliency Map

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Huu Ton Le, Thierry Urruty, Syntyche Gbehounou, François Lecellier, Jean Martinet, et al.. Improving retrieval framework using information gain models. Signal, Image and Video Processing, 2017, 11 (2), pp.309-316. ⟨10.1007/s11760-016-0938-x⟩. ⟨hal-01515952⟩