NDLI: Bayesian relevance feedback for content-based image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Vasconcelos, N. Lippman, A.
Copyright Year	2000
Description	Author affiliation: Media Lab., MIT, MA, USA (Vasconcelos, N.)
Abstract	We present a Bayesian learning algorithm that relies on belief propagation to integrate feedback provided by the user over a retrieval session. Bayesian retrieval leads to a natural criteria for evaluating local image similarity without requiring any image segmentation. This allows the practical implementation of retrieval systems where users can provide image regions, or objects, as queries. Region-based queries are significantly less ambiguous than queries based on entire images leading to significant improvements in retrieval precision. When combined with local similarity, Bayesian belief propagation is a powerful paradigm for user interaction. Experimental results show that significant improvements in the frequency of convergence to the relevant images can be achieved by the inclusion of learning in the retrieval process.
Starting Page	63
Ending Page	67
File Size	184176
Page Count	5
File Format	PDF
ISBN	076950695X
DOI	10.1109/IVL.2000.853841
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-06-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayes procedures
Content Type	Text
Resource Type	Article

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