Object localization by propagating connectivity via superfeatures

Ishani Chakraborty, Ahmed Elgammal

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

Abstract

In this paper, we propose a part-based approach to localize objects in cluttered images. We represent object parts as boundary segments and image patches. A semi-local grouping of parts named superfeatures encodes appearance and connectivity within a neighborhood. To match parts, we integrate inter-feature similarities and intra-feature connectivity via a relaxation labeling framework. Additionally, we use a global elliptical shape prior to match the shape of the solution space to that of the object. To this end, we demonstrate the efficacy of the method for detecting various objects in cluttered images by comparing them to simple object models.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages3069-3072
Number of pages4
DOIs
StatePublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
Country/TerritoryTurkey
CityIstanbul
Period8/23/108/26/10

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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