| Paper Details: | Downloads: 382 |
| Serial Number: | P1150509001
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| Title: | Automatic Relevance Feedback for Distributed Content-Based Image Retrieval
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| Authors: | Ivan Lee and Paisarn Muneesawang and Ling Guan
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| 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.
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| Keywords: | Content Based Image Retrieval, Distributed Database, Fuzzy Algorithms, Self Organizing Tree Map.
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| Journal/Conference: | International Journal of Graphics, Vision and Image Processing
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| Volume: | 5
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| Issue: | 4
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| Submission Date: | 2/1/2005 12:00:00 AM
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| Review Date: | 3/1/2005 12:00:00 AM
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| Publishing Date: | 4/1/2005 12:00:00 AM
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| Article Downloads: | 382
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