Weighted pseudo-metric for a fast CBIR method

Authors:
R. Ksantini;D. Ziou;B. Colin;F. Dubeau
Affiliations:
Department of Mathematics, Faculity of Sciences, University of Sherbrooke, Sherbrooke, QC, Canada;Department of Mathematics, Faculity of Sciences, University of Sherbrooke, Sherbrooke, QC, Canada;Department of Mathematics, Faculity of Sciences, University of Sherbrooke, Sherbrooke, QC, Canada;Department of Mathematics, Faculity of Sciences, University of Sherbrooke, Sherbrooke, QC, Canada
Venue:
Machine Graphics & Vision International Journal
Year:
2006

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Abstract

In this paper, a simple and fast querying method for content-based image retrieval is presented. In order to measure the similarity degree between two color images both quickly and effectively, we use a weighted pseudo-metric employing one-dimensional Daubechies decomposition and compression of the extracted feature vectors. In order to improve the discriminatory capacity of the pseudo-metric, we compute its weights using separately a classical logistic regression model and a Bayesian logistic regression model. The Bayesian logistic regression model was shown to be significantly better than the classical logistic regression model at improving the retrieval performance. Experimental results are reported on the WANG and ZuBuD color image databases proposed by [11].