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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Goldmann, L., Thiele, L., Sikora, T. (2006). Online Image Retrieval System Using Long Term Relevance Feedback. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_43
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DOI: https://doi.org/10.1007/11788034_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36018-6
Online ISBN: 978-3-540-36019-3
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