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Automatic Relevance Feedback for Distributed Content-Based Image Retrieval
Ivan Lee (1) , Paisarn Muneesawang (2) , Ling Guan (3)
(1) Department of Electrical and Information Engineering, University of Sydney,
Sydney , NSW 2006, Australia
(2) Department College of Information Technology, United Arab Emirates University,
Al-Ain , United Arab Emirates
(3) Department of Electrical and Computer Engineering, Ryerson University ,
Toronto , ON M5B 2K3 , Canada
Abstract:
In this paper, we present the machine-controlled relevance feedback technique for the distributed content-based image retrieval (CBIR) system. A nonlinear model based on the Gaussian-shaped radial basis function (RBF) is applied in the feedback process, and a bias weighting is introduced to the query content as the partial supervised function to improve the retrieval precision. This paper introduces a decentralized Peer-to-Peer CBIR algorithm which reinforces offline feature calculation technique to generate a distributed feature descriptor database (DFDD), to offload feature computation to the P2P network while improving the retrieval precisions. In addition, this paper compares the retrieval performance over centralized, clustered, and decentralized peer-to-peer network topologies. Combination of the ARF technique and the distributed CBIR system eliminates the human intervention, hence automates distributed CBIR in a hierarchical manner.
Key words:
Content Based Image Retrieval,
Distributed Database, Fuzzy Algorithms, Self Organizing
Tree Map.
BibTex:
@ARTICLE{P1150509001,
AUTHOR = {Ivan Lee and Paisarn Muneesawang and Ling Guan},
TITLE = {Automatic Relevance Feedback for Distributed Content-Based Image Retrieval}, JOURNAL = {ICGST International Journal on Graphics, Vision and Image Processing}, YEAR = {2005}, MONTH = {April}, VOLUME = {05}, ISSUE = {4}, PAGES = {15--24} } (
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