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FAST: Toward more effective and efficient image retrieval

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Abstract

This paper focuses 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 (HEAR) algorithm 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 feedback to tailor the effective 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 great promise in delivering fast and semantics-tailored image retrieval in CBIR.

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  1. Department of Computer Science, Sate University of New York at Binghamton, Binghamton, NY, 13902, USA

    Ruofei Zhang & Zhongfei Mark Zhang

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  1. Ruofei Zhang
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Zhang, R., Zhang, Z.M. FAST: Toward more effective and efficient image retrieval. Multimedia Systems 10, 529–543 (2005). https://doi.org/10.1007/s00530-005-0180-9

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