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Abstract: Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are constrained to be spherically symmetric and their number has to be known a priori. In nonparametric clustering methods, like the one based on mean shift, these limitations are eliminated but the amount of computation becomes prohibitively large as the dimension of the space increases. We exploit a recently proposed... (Update)
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BibTeX entry: (Update)
B. Georgescu, I. Shimshoni, and P. Meer. Mean shift based clustering in high dimensions: A texture classification example. In Proc. Int. Conf. Comp. Vision, pages 456--463, 2003. http://citeseer.ist.psu.edu/article/georgescu03mean.html More
@misc{ georgescu03mean,
author = "B. Georgescu and I. Shimshoni and P. Meer",
title = "Mean shift based clustering in high dimensions: A texture classification
example",
text = "B. Georgescu, I. Shimshoni, and P. Meer. Mean shift based clustering in
high dimensions: A texture classification example. In Proc. Int. Conf. Comp.
Vision, pages 456--463, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/article/georgescu03mean.html" }
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