Improving retrieval framework using information gain models. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Le, Huu; Urruty, Thierry; Gbèhounou, Syntyche; Lecellier, François; Martinet, Jean; Fernandez-Maloigne, Christine
• Is Part Of: Signal, image and video processing. Volume 11:Issue 2(2017)
• Page Start: 309
• Page End: 316
• Publication Date: 2017-02
• Subjects: Image retrieval -- Vocabulary construction -- Bags of visual words -- Saliency map; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0938-x
• Languages: English
• ISSNs: 1863-1703

Abstract

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.