by Stommel, Martin and Herzog, Otthein
Abstract:
It is shown that distance computations between SIFT-descriptors using the Euclidean distance su er from the curse of dimensionality. The search for exact matches is less a ected than the generalisation of image patterns, e.g. by clustering methods. Experimental results indicate that for the case of generalisation, the Hamming distance on binarised SIFT-descriptors is a much better choice.
Reference:
Stommel, Martin and Herzog, Otthein, "Binarising SIFT-Descriptors to Reduce the Curse of Dimensionality in Histogram-Based Object Recognition", In SIP, Springer, no. 61, Jeju Island, Korea, pp. 320–328, 2009.
Bibtex Entry:
@INPROCEEDINGS{Stommel2009,
author = {Stommel, Martin and Herzog, Otthein},
title = {Binarising SIFT-Descriptors to Reduce the Curse of Dimensionality
in Histogram-Based Object Recognition},
booktitle = {SIP},
year = {2009},
editor = {Slezak, Dominik and Pal, Sankar K. and Kang, Byeong-Ho and Gu, Junzhong
and Kurada, Hideo and Kim, Tai-hoon},
number = {61},
series = {Communications in Computer and Information Science},
pages = {320--328},
address = {Jeju Island, Korea},
month = {December10--12},
publisher = {Springer},
abstract = {It is shown that distance computations between SIFT-descriptors using
the Euclidean distance su er from the curse of dimensionality. The
search for exact matches is less a ected than the generalisation
of image patterns, e.g. by clustering methods. Experimental results
indicate that for the case of generalisation, the Hamming distance
on binarised SIFT-descriptors is a much better choice.},
isbn = {ISSN 1865-0929},
owner = {pmania},
timestamp = {2012.11.06}
}