NDLI: A feature re-weighting approach for relevance feedback in image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Yimin Wu Aidong Zhang
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci. & Eng., State Univ. of New York, USA (Yimin Wu; Aidong Zhang)
Abstract	Users of image databases often prefer to retrieve relevant images by categories. Unfortunately, images are usually indexed by low-level features like color, texture and shape, which often fail to capture high-level concepts well. To address this issue, relevance feedback has been extensively used to associate low-level image features with highlevel concepts. Among all existing relevance feedback approaches, query movement and feature re-weighting have been proven to be suitable for large-scaled image databases with high dimensional image features. We present a feature re-weighting approach using relevant images as well as irrelevant ones in the relevance feedback. As far as feature re-weighting approaches are concerned, one of their common drawbacks is that the feature re-weighting process is prone to be trapped by suboptimal states. To overcome this problem, we introduce a disturbing factor, which is based on the Fisher criterion, to push the feature weights out of sub-optimum. Experimental results on a large-scaled image database with 31,438 COREL images demonstrate the effectiveness of the presented method.
Sponsorship	IEEE Signal Process. Soc
File Size	375936
File Format	PDF
ISBN	0780376226
ISSN	15224880
DOI	10.1109/ICIP.2002.1040017
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feedback Image retrieval Image databases Spatial databases Information retrieval Shape Content based retrieval Computer science Data engineering Bridges
Content Type	Text
Resource Type	Article

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