Riadh Ksantini, Djemel Ziou, Bernard Colin, Francois Dubeau
Distance measures like the Euclidean distance have been the most widely used to measure similarities between feature vectors in the contentbased image retrieval (CBIR) systems. However in these similarity measures no assumption is made about the probability distributions and the local relevances of the feature vectors. Therefore irrelevant features might hurt retrieval performance. Probabilistic approaches have proven to be an effective solution to this CBIR problem. In this paper we use a Bayesian logistic regression model in order to compute the weights of a pseudometric to improve its discriminatory capacity and then to increase image retrieval accuracy. The pseudometric weights were adjusted by the classical logistic regression model. The Bayesian logistic regression model was shown to be a significantly better tool than the classical logistic regression one to improve the retrieval performance. The retrieval method is fast and is based on feature selection. Experimental results are reported on the Zubud and WANG color image databases.
Subjects: 1.10 Information Retrieval; 3.4 Probabilistic Reasoning
Submitted: Oct 15, 2006