Motion segmentation by SCC on the Hopkins 155 database

Guangliang Chen, Gilad Lerman

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

17 Scopus citations

Abstract

We apply the Spectral Curvature Clustering (SCC) algorithm to a benchmark database of 155 motion sequences, and show that it outperforms all other state-of-the-art methods. The average misclassification rate by SCC is 1.41% for sequences having two motions and 4.85% for three motions.

Original languageEnglish (US)
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Pages759-764
Number of pages6
DOIs
StatePublished - 2009
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
Duration: Sep 27 2009Oct 4 2009

Publication series

Name2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Other

Other2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Country/TerritoryJapan
CityKyoto
Period9/27/0910/4/09

Fingerprint

Dive into the research topics of 'Motion segmentation by SCC on the Hopkins 155 database'. Together they form a unique fingerprint.

Cite this