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Performing Content-Based Image Retrieval (CBIR) from Internet databases connected through Peer-to-Peer (P2P) network, abbreviated as P2P-CBIR, helps to effectively explore the large-scale image database distributed over connected peers. Decentralized unstructured P2P framework is adopted in our system to compromise with the structured one while still reserving flexible routing control when peers join/leave or network fails. The P2P-CBIR search engine is designed to provide multi-instance query with multi-feature types to effectively reduce network traffic and maintain high retrieval accuracy. The proposed P2P-CBIR system is also designed to provide scalable retrieval control, which can adaptively control the query scope and progressively refine the accuracy of retrieved results. We also proposed to provide the most updated local database characteristics for the P2P-CBIR users. By reconfiguring system at each regular interval time, it can effectively reduce trivial peer routing and retrieval operations due to imprecise configuration. Experiments demonstrated that the average recall rate of the proposed P2P-CBIR with reconfiguration is higher than the one without about 20%, and the latter outperforms previous methods, i.e., firework query model (FQM) and breadth-first search (BFS) about 20% and 120%, respectively, under the same range of TTL values.