A relevance feedback approach for content based image retrieval using gaussian mixture models

Authors:
Apostolos Marakakis;Nikolaos Galatsanos;Aristidis Likas;Andreas Stafylopatis
Affiliations:
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Department of Computer Science, University of Ioannina, Ioannina, Greece;Department of Computer Science, University of Ioannina, Ioannina, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

In this paper a new relevance feedback (RF) methodology for content based image retrieval (CBIR) is presented. This methodology is based on Gaussian Mixture (GM) models for images. According to this methodology, the GM model of the query is updated in a probabilistic manner based on the GM models of the relevant images, whose relevance degree (positive or negative) is provided by the user. This methodology uses a recently proposed distance metric between probability density functions (pdfs) that can be computed in closed form for GM models. The proposed RF methodology takes advantage of the structure of this metric and proposes a method to update it very efficiently based on the GM models of the relevant and irrelevant images characterized by the user. We show with experiments the merits of the proposed methodology.