Dynamic Partial Function (DPF), which dynamically selects a subset of features to measure pairwise image similarity, has been shown to be very effective in near-replica image recognition. DPF, however, suffers from the one-size-fits-all problem: it requires that all pairwise similarity measurements must use the same number of features. We propose methods for enhancing DPF's performance by allowing different numbers of features to be selected in a pairwise manner. Through extensive empirical studies, we show that our three schemes: thresholding, sampling and weighting, and hybrid schemes of these three basic approaches, substantially outperform DPF in near-replica image recognition.


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    Title :

    Enhancing DPF for near-replica image recognition


    Contributors:
    Yan Meng, (author) / Chang, E. (author) / Beitao Li, (author)


    Publication date :

    2003-01-01


    Size :

    4134521 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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