Mean shift based clustering in high dimensions: A texture classification example

Bogdan Georgescu, Ilan Shimshoni, Peter Meer

Research output: Contribution to conferencePaperpeer-review

346 Scopus citations

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 orfe 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 approximation technique, locality-sensitive hashing (LSH), to reduce the computational complexity of adaptive mean shift. In our implementation of LSH the optimal parameters of the data structure are determined by a pilot learning procedure, and the partitions are data driven. As an application, the performance of mode and k-means based textons are compared in a texture classification study.

Original languageEnglish (US)
Pages456-463
Number of pages8
DOIs
StatePublished - 2003
EventProceedings: Ninth IEEE International Conference on Computer Vision - Nice, France
Duration: Oct 13 2003Oct 16 2003

Other

OtherProceedings: Ninth IEEE International Conference on Computer Vision
CountryFrance
CityNice
Period10/13/0310/16/03

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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