Online image retrieval system using long term relevance feedback

Authors:
Lutz Goldmann;Lars Thiele;Thomas Sikora
Affiliations:
Communication Systems Group, Technical University of Berlin, Berlin, Germany;Communication Systems Group, Technical University of Berlin, Berlin, Germany;Communication Systems Group, Technical University of Berlin, Berlin, Germany
Venue:
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Year:
2006

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Abstract

This paper describes an original system for content based image retrieval. It is based on MPEG-7 descriptors and a novel approach for long term relevance feedback using a Bayesian classifier. Each image is represented by a special model that is adapted over multiple feedback rounds and even multiple sessions or users. The experiments show its outstanding performance in comparison to often used short term relevance feedback and the recently proposed FIRE system.