Fast Query Point Movement Techniques for Large CBIR Systems

    Authors

    D. Z. Liu; K. A. Hua; K. Vu;N. Yu

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Knowl. Data Eng.

    Keywords

    Content-based image retrieval; relevance feedback; target search; index; structures; IMAGE RETRIEVAL-SYSTEM; RELEVANCE FEEDBACK; DATABASES; Computer Science, Artificial Intelligence; Computer Science, Information; Systems; Engineering, Electrical & Electronic

    Abstract

    Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques, designed around query refinement based on relevance feedback (RF), suffer from slow convergence, and do not guarantee to find intended targets. To address these limitations, we propose several efficient query point movement methods. We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency. We also consider strategies to minimize the effects of users' inaccurate RF. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of required iterations and improves overall retrieval performance. The experimental results also confirm that our approach can always retrieve intended targets even with poor selection of initial query points.

    Journal Title

    Ieee Transactions on Knowledge and Data Engineering

    Volume

    21

    Issue/Number

    5

    Publication Date

    1-1-2009

    Document Type

    Article

    Language

    English

    First Page

    729

    Last Page

    743

    WOS Identifier

    WOS:000264300600009

    ISSN

    1041-4347

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