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A performance evaluation of local descriptors

, and . Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27 (10): 1615 -1630 (October 2005)
DOI: 10.1109/TPAMI.2005.188

Abstract

In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector Mikolajczyk, K and Schmid, C, 2004. Many different descriptors have been proposed in the literature. 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 Belongie, S, et al., April 2002, steerable filters Freeman, W and Adelson, E, Setp. 1991, PCA-SIFT Ke, Y and Sukthankar, R, 2004, differential invariants Koenderink, J and van Doorn, A, 1987, spin images Lazebnik, S, et al., 2003, SIFT Lowe, D. G., 1999, complex filters Schaffalitzky, F and Zisserman, A, 2002, moment invariants Van Gool, L, et al., 1996, 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.

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IEEE Xplore - A performance evaluation of local descriptors

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