Improving retrieval framework using information gain models. Issue 2 (February 2017) - Le, Huu; Urruty, Thierry; Gbèhounou, Syntyche; Lecellier, François; Martinet, Jean; Fernandez-Maloigne, Christine
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.