Learning structural element patch models with hierarchical palettes

Jeroen Chua, Inmar Givoni, Ryan Adams, Brendan Frey

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

8 Scopus citations

Abstract

Image patches can be factorized into shapelets that describe segmentation patterns called structural elements (stels), and palettes that describe how to paint the shapelets. We introduce local palettes for patches, global palettes for entire images and universal palettes for image collections. Using a learned shapelet library, patches from a test image can be analyzed using a variational technique to produce an image descriptor that represents local shapes and colors separately. We show that the shapelet model performs better than SIFT, Gist and the standard stel method on Caltech28 and is very competitive with other methods on Caltech101.

Original languageEnglish (US)
Title of host publication2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Pages2416-2423
Number of pages8
DOIs
StatePublished - Oct 1 2012
Externally publishedYes
Event2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 - Providence, RI, United States
Duration: Jun 16 2012Jun 21 2012

Publication series

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

Other

Other2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
CountryUnited States
CityProvidence, RI
Period6/16/126/21/12

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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