NDLI: A Framework of CBIR System Based on Relevance Feedback

Content Provider	IEEE Xplore Digital Library
Author	Jianjiang Lu Zhenghui Xie Ran Li Yafei Zhang Jiabao Wang
Copyright Year	2009
Abstract	Content-based image retrieval (CBIR) is an effective approach for obtaining desired image, however, due to the semantic gap between low-level visual features and high-level concept of image, CBIR system of state-of-the-art always can’t achieve satisfying retrieval performance. In this paper, we propose a novel CBIR system framework. In order to bridge the semantic gap, the mechanism of relevance feedback is involved in the system. More various features are included at low level, which can provide more abundant image content description. A bi-coded chromosome based genetic algorithm is performed to obtain optimal features and relevant optimal weights based on users’ relevance feedback. With the optimal feature set and optimal weights, the similarity between image in original searching results and query image is considered to be the main factor of rank score.
Starting Page	175
Ending Page	178
File Size	343207
Page Count	4
File Format	PDF
ISBN	9780769538594
DOI	10.1109/IITA.2009.99
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	State feedback Automation Digital images Image retrieval genetic selection Information retrieval re-ranking Information technology Biological cells Genetic algorithms Radio access networks relevance feedback Programmable logic arrays CBIR
Content Type	Text
Resource Type	Article

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