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A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS (2005)

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by Krystian Mikolajczyk , Cordelia Schmid
Citations:1782 - 51 self
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BibTeX

@MISC{Mikolajczyk05aperformance,
    author = {Krystian Mikolajczyk and Cordelia Schmid},
    title = { A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS },
    year = {2005}
}

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Abstract

In this paper we compare the performance of descriptors computed for local interest regions, as for example extracted by the Harris-Affine detector [32]. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context [3], steerable filters [12], PCA-SIFT [19], differential invariants [20], spin images [21], SIFT [26], complex filters [37], moment invariants [43], and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor, and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors.

Keyphrases

performance evaluation local descriptor    steerable filter    interest region detector    criterion recall    differential invariant    time robust    sift descriptor    different type    local interest region    complex filter    spin image    low dimensional descriptor    many different descriptor    interest region    moment invariant    different image transformation    original method    shape context    harris-affine detector   

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