Combining diversity-based active learning with discriminant analysis in image retrieval

Charlie K. Dagli, Shyamsundar Rajaram, Thomas S Huang

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

Abstract

Small-sample learning in image retrieval is a pertinent and interesting problem. Relevance feedback is an active area of research that seeks to find algorithms that are robust with only a small number of examples. Much work has been done in both the machine learning and pattern recognition communities to develop algorithms that learn a high-level semantic concept in a low-level image feature space. In this paper we seek to leverage techniques from both these communities to explore a hybrid relevance feedback system which combines the insight gained from, discriminant analysis and active learning. Our technique uses a diversity-based pool-query technique along with biased discriminant analysis to improve the query refinement process. Comparative results are observed and thoughts for future work are presented.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005
Pages173-178
Number of pages6
DOIs
StatePublished - Dec 1 2005
Event3rd International Conference on Information Technology and Applications, ICITA 2005 - Sydney, Australia
Duration: Jul 4 2005Jul 7 2005

Publication series

NameProceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005
VolumeI

Other

Other3rd International Conference on Information Technology and Applications, ICITA 2005
Country/TerritoryAustralia
CitySydney
Period7/4/057/7/05

ASJC Scopus subject areas

  • Engineering(all)

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