NDLI: Integrating User Feedback Log into Relevance Feedback by Coupled SVM for Content-Based Image Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Hoi, S.C.H. Lyu, M.R. Rong Jin
Copyright Year	2005
Description	Author affiliation: The Chinese University of Hong Kong (Hoi, S.C.H.)
Abstract	Relevance feedback has been shown as an important tool to boost the retrieval performance in content-based image retrieval. In the past decade, various algorithms have been proposed to formulate relevance feedback in contentbased image retrieval. Traditional relevance feedback techniques mainly carry out the learning tasks by focusing lowlevel visual features of image content with little consideration on log information of user feedback. However, from a long-term learning perspective, the user feedback log is one of the most important resources to bridge the semantic gap problem in image retrieval. In this paper we propose a novel technique to integrate the log information of user feedback into relevance feedback for image retrieval. Our algorithms construction is based on a coupled support vector machine which learns consistently with the two types of information: the low-level image content and the user feedback log. We present a mathematical formulation of the problem and develop a practical algorithm to solve the problem effectively. Experimental results show that our proposed scheme is effective and promising.
Starting Page	1177
Ending Page	1177
File Size	334950
Page Count	1
File Format	PDF
ISBN	0769526578
DOI	10.1109/ICDE.2005.233
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Bridges State feedback Content based retrieval Image retrieval Focusing Multimedia databases Information retrieval Feature extraction
Content Type	Text
Resource Type	Article

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