The bottleneck geodesic: Computing pixel affinity

Ido Orner*, Michael Werman

*Corresponding author for this work

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

10 Scopus citations

Abstract

A meaningful affinity measure between pixels is essential for many computer vision and image processing applications. We propose an algorithm that works in the features' histogram to compute image specific affinity measures. We use the observation that clusters in the feature space are typically smooth, and search for a path in the feature space between feature points that is both short and dense. Failing to find such a path indicates that the points are separated by a bottleneck in the histogram and therefore belong to different clusters. We call this new affinity measure the "Bottleneck Geodesic". Empirically we demonstrate the superior results achieved by using our affinities as opposed to those using the widely used Euclidean metric, traditional geodesics and the simple bottleneck.

Original languageAmerican English
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages1901-1907
Number of pages7
DOIs
StatePublished - 2006
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: 17 Jun 200622 Jun 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
ISSN (Print)1063-6919

Conference

Conference2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Country/TerritoryUnited States
CityNew York, NY
Period17/06/0622/06/06

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