A Performance Evaluation of Local Descriptors Export

@article{MIK04, 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. 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.}, address = {Washington, DC, USA}, author = {Mikolajczyk, Krystian and Schmid, Cordelia}, citeulike-article-id = {1100945}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1083822.1083989}, citeulike-linkout-1 = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.188}, citeulike-linkout-2 = {http://dx.doi.org/10.1109/TPAMI.2005.188}, citeulike-linkout-3 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1498756}, doi = {10.1109/TPAMI.2005.188}, issn = {0162-8828}, journal = {IEEE Trans. Pattern Anal. Mach. Intell.}, keywords = {artificial, benchmark, descriptor, evaluation, sift, vision}, month = {October}, number = {10}, pages = {1615--1630}, posted-at = {2008-12-14 14:50:52}, priority = {0}, publisher = {IEEE Computer Society}, title = {A Performance Evaluation of Local Descriptors}, url = {http://dx.doi.org/10.1109/TPAMI.2005.188}, volume = {27}, year = {2005} }

TY - JOUR ID - MIK04 L3 - citeulike-article-id:1100945 N2 - 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. 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. IS - 10 JF - IEEE Trans. Pattern Anal. Mach. Intell. SN - 0162-8828 AD - Washington, DC, USA EP - 1630 TI - A Performance Evaluation of Local Descriptors PB - IEEE Computer Society VL - 27 SP - 1615 KW - artificial KW - benchmark KW - descriptor KW - evaluation KW - sift KW - vision AU - Mikolajczyk, Krystian AU - Schmid, Cordelia PY - 2005/10// UR - http://dx.doi.org/10.1109/TPAMI.2005.188 ER -