Model indexing: The graph-hashing approach

H. Sossa, R. Horaud

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations

Abstract

The problem of object recognition in computer vision is addressed. A method for model indexing, which, given a group of image features, rapidly extracts from the list of objects those objects containing this group of features, is presented. The method operates on an abstract representation of features, more precisely, groups of features. In practice, this abstract representation takes the form of a graph. The present study deals with binary graphs only, that is, only one feature-type and one feature-relationship-type can be embedded in the representation.

Original languageEnglish
Title of host publicationProceedings CVPR 1992 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages811-814
Number of pages4
ISBN (Electronic)0818628553
DOIs
StatePublished - 1992
Externally publishedYes
Event1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992 - Champaign, United States
Duration: 15 Jun 199218 Jun 1992

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1992-June
ISSN (Print)1063-6919

Conference

Conference1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992
Country/TerritoryUnited States
CityChampaign
Period15/06/9218/06/92

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