Many recent content-based image retrieval techniques utilize relevance feedback (RF) from the user to adjust the system response to better meet user expectations. One school of RF-based methods uses a weighted Minkowski distance metric to assess similarity, and adjusts the weights to refine query response. A new method of estimating these weight vectors is presented which outperforms existing methods, particularly for the important case of limited training data. A new objective function is presented for an iterative optimization routine which more closely aligns optimization goals with true system goals. A new analysis framework is presented in the derivation of this technique which is useful for understanding the limitations of many RF methods.


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    Title :

    A new analysis framework for relevance feedback-driven similarity measure refinement in content-based image retrieval


    Contributors:
    Jones, B.C. (author) / Wilkes, D.M. (author)


    Publication date :

    2001-01-01


    Size :

    591678 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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