| Summary: | 博士 === 國立臺灣科技大學 === 電機工程系 === 102 === In this thesis, we proposed to perform content-based image retrieval (CBIR) on Internet scale databases connected through peer-to-peer (P2P) networks, abbreviated as P2P-CBIR, which utilizes an intelligent preprocessing to identify the object regions and provides scalable retrieval function. For preprocessing, we proposed a dual multiScalE Graylevel mOrphological open and close recoNstructions (SEGON) algorithm, and utilized edge coverage rate to segment foreground (FG) object regions in one image. To improve FG object segmentation accuracy, a background (BG) gray-level variation mesh is built. The SEGON was developed from a macroscopic perspective on image BG gray levels and implemented through regular procedures to deal with large-scale database images. To evaluate the segmentation accuracy, the probability of coherent segmentation labeling, i.e., the normalized probability random index (PRI), between a computer-segmented image and the hand-labeled one is computed for comparisons. Experiments showed that the proposed object segmentation method outperforms others in the PRI performance. The normalized correlation coefficient of features among query samples was computed to integrate the similarity ranks of different features in order to perform multi-instance queries with multiple features (MIMF). Retrieval precision–recall (PR) and rank performances, with and without SEGON, were compared. Performing SEGON-enabled CBIR on large-scale databases yields higher PR performance.
For performing Internet scale CBIR, a P2P-CBIR system has been proposed, which helps to effectively explore the large-scale image database distributed over connected peers. The decentralized unstructured P2P network topology is adopted to compromise with the structured one, and informed-like instead of blind-like searching approach enables flexible routing control when peers join/leave or network fails. The P2P-CBIR adopts MIMF to reduce average network traffics while maintaining high retrieval accuracy on the query peer. In addition, scalable retrieval control can also be developed based on the P2P-CBIR framework, which can adapt the query scope and progressively refine the accuracy during the retrieval process. We also proposed to record instant local database characteristics of peers for the P2P-CBIR system to update peer linking information. By reconfiguring system at each regular interval time, we can effectively reduce trivial peer routing and retrieval operations due to
imprecise configurations. We also proposed to optimally configure the P2P-CBIR system such that, under a certain number of online users, which would yield the highest recall rate. Experiments show that the average recall rate of the proposed P2P-CBIR method with reconfiguration is higher than that of the one without, and the latter outperforms previous methods, under the same retrieval scope, i.e., same time-to-live (TTL) settings. Furthermore, simulations demonstrate that, with the optimal configuration, recall rates can be improved while the network traffic of each peer is reduced, under the same number of on-line users.
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