Learning discriminative dictionaries with partially labeled data

Ashish Shrivastava, Jaishanker K. Pillai, Vishal M. Patel, Rama Chellappa

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

37 Scopus citations

Abstract

While recent techniques for discriminative dictionary learning have demonstrated tremendous success in image analysis applications, their performance is often limited by the amount of labeled data available for training. Even though labeling images is difficult, it is relatively easy to collect unlabeled images either by querying the web or from public datasets. In this paper, we propose a discriminative dictionary learning technique which utilizes both labeled and unlabeled data for learning dictionaries. Extensive evaluation on existing datasets demonstrate that the proposed method performs significantly better than state of the art dictionary learning approaches when unlabeled images are available for training.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages3113-3116
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States
Duration: Sep 30 2012Oct 3 2012

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2012 19th IEEE International Conference on Image Processing, ICIP 2012
Country/TerritoryUnited States
CityLake Buena Vista, FL
Period9/30/1210/3/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

Keywords

  • Semi-supervised dictionary learning
  • classification
  • latent variables

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