This work is about Content Based Image Retrieval (CBIR), focusing on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region based approach to improving the retrieval effectiveness and robustness (2) development of a new hierarchical indexing structure and the corresponding Hierarchical, Elimination-based A* Retrieval algorithm (HEAR) to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case (3) employment of user relevance feedbacks to tailor the semantic retrieval to each user's individualized query preference through the novel Indexing Tree Pruning (ITP) and Adaptive Region Weight Updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds a great promise in delivering fast and semantics-tailored image retrieval in CBIR.
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