TY - GEN
T1 - A linear time histogram metric for improved SIFT matching
AU - Pele, Ofir
AU - Werman, Michael
PY - 2008
Y1 - 2008
N2 - We present a new metric between histograms such as SIFT descriptors and a linear time algorithm for its computation. It is common practice to use the L 2 metric for comparing SIFT descriptors. This practice assumes that SIFT bins are aligned, an assumption which is often not correct due to quantization, distortion, occlusion etc. In this paper we present a new Earth Mover's Distance (EMD) variant. We show that it is a metric (unlike the original EMD [1] which is a metric only for normalized histograms). Moreover, it is a natural extension of the L 1 metric. Second, we propose a linear time algorithm for the computation of the EMD variant, with a robust ground distance for oriented gradients. Finally, extensive experimental results on the Mikolajczyk and Schmid dataset [2] show that our method outperforms state of the art distances.
AB - We present a new metric between histograms such as SIFT descriptors and a linear time algorithm for its computation. It is common practice to use the L 2 metric for comparing SIFT descriptors. This practice assumes that SIFT bins are aligned, an assumption which is often not correct due to quantization, distortion, occlusion etc. In this paper we present a new Earth Mover's Distance (EMD) variant. We show that it is a metric (unlike the original EMD [1] which is a metric only for normalized histograms). Moreover, it is a natural extension of the L 1 metric. Second, we propose a linear time algorithm for the computation of the EMD variant, with a robust ground distance for oriented gradients. Finally, extensive experimental results on the Mikolajczyk and Schmid dataset [2] show that our method outperforms state of the art distances.
UR - http://www.scopus.com/inward/record.url?scp=56749170360&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-88690-7_37
DO - 10.1007/978-3-540-88690-7_37
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AN - SCOPUS:56749170360
SN - 3540886893
SN - 9783540886891
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 495
EP - 508
BT - Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
PB - Springer Verlag
T2 - 10th European Conference on Computer Vision, ECCV 2008
Y2 - 12 October 2008 through 18 October 2008
ER -