Semi-automated relevance feedback for distributed content based image retrieval
Ivan Lee, Ling Guan · 2005
Retrieving images according to the semantic meanings is a challenging problem, mainly due to the complexity of mapping semantic meanings to low-level descriptors. Such complexity raises the scalability issue, especially when the database is distributed over multiple servers such as the peer-to-peer network. To address the scalability issue, we present an approach for content-based image retrieval (CBIR) over a distributed peer-to-peer network. The proposed system features: (1) improved retrieval precision; (2) decentralized database for high availability; (3) decentralized processing to utilize the computation resources. On the proposed peer-to-peer retrieval system, we present (1) query node based and (2) agent based approaches for on-demand advanced-feature calculation. Finally, we present the analysis for semi-automated relevance feedback over the peer-to-peer CBIR framework.