Modeling appearances with low-rank SVM

Lior Wolf*, Hueihan Jhuang, Tamir Hazan

*Corresponding author for this work

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

Abstract

Several authors have noticed that the common representation of images as vectors is sub-optimal The process of vectorization eliminates spatial relations between some of the nearby image measurements and produces a vector of a dimension which is the product of the measurements' dimensions. It seems that images may be better represented when taking into account their structure as a 2D (or multi-D) array. Our work bears similarities to recent work such as 2DPCA or Coupled Subspace Analysis in that we treat images as 2D arrays. The main difference, however, is that unlike previous work which separated representation from the discriminative learning stage, we achieve both by the same method. Our framework, "Low-Rank separators", studies the use of a separating hyperplane which are constrained to have the structure of low-rank matrices. We first prove that the low-rank constraint provides preferable generalization properties. We then define two "Low-rank SVMproblems" and propose algorithms to solve these. Finally, we provide supporting experimental evidence for the framework.

Original languageEnglish
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
PublisherIEEE Computer Society
ISBN (Print)1424411807, 9781424411801
DOIs
StatePublished - 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: 17 Jun 200722 Jun 2007

Publication series

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

Conference

Conference2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Country/TerritoryUnited States
CityMinneapolis, MN
Period17/06/0722/06/07

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