Training image classifiers with similarity metrics, linear programming, and minimal supervision

Karl Ni, Ethan Phelps, Katherine L. Bouman, Nadya Bliss

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

Abstract

Image classification is a classical computer vision problem with applications to semantic image annotation, querying, and indexing. Recent and effective generative techniques assume Gaussianity, rely on distance metrics, and estimate distributions, but are unfortunately not convex nor keep computational architecture in mind. We propose image content classification through convex linear programming using similarity metrics rather than commonly-used Mahalanobis distances. The algorithm is solved through a hybrid iterative method that takes advantage of optimization space properties. Our optimization problem uses dot products in the feature space exclusively, and therefore can be extended to non-linear kernel functions in the transductive setting.

Original languageEnglish (US)
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Pages1979-1983
Number of pages5
DOIs
StatePublished - Dec 1 2012
Externally publishedYes
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
CountryUnited States
CityPacific Grove, CA
Period11/4/1211/7/12

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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