Clustering appearances of 3D objects

Ronen Basri, Dan Roth, David Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce a method for unsupervised clustering of images of 3D objects. Our method examines the space of all images and partitions the images into sets that form smooth and parallel surfaces in this space. It further uses sequences of images to obtain more reliable clustering. Finally, since our method relies on a non-Euclidean similarity measure we introduce algebraic techniques for estimating local properties of these surfaces without first embedding the images in a Euclidean space. We demonstrate our method by applying it to a large database of images.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Pages414-420
Number of pages7
DOIs
StatePublished - 1998
EventProceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Santa Barbara, CA, USA
Duration: Jun 23 1998Jun 25 1998

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

OtherProceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CitySanta Barbara, CA, USA
Period6/23/986/25/98

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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