Leveraging active learning for relevance feedback using an information theoretic diversity measure

Charlie K. Dagli, Shyamsundar Rajaram, Thomas S. Huang

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

Abstract

Interactively learning from a small sample of unlabeled examples is an enormously challenging task. Relevance feedback and more recently active learning are two standard techniques that have received much attention towards solving this interactive learning problem. How to best utilize the user's effort for labeling, however, remains unanswered. It has been shown in the past that labeling a diverse set of points is helpful, however, the notion of diversity has either been dependent on the learner used, or computationally expensive. In this paper, we intend to address these issues by proposing a fundamentally motivated, information-theoretic view of diversity and its use in a fast, non-degenerate active learning-based relevance feedback setting. Comparative testing and results are reported and thoughts for future work are presented.

Original languageEnglish (US)
Title of host publicationImage and Video Retrieval - 5th International Conference, CIVR 2006, Proceedings
PublisherSpringer-Verlag
Pages123-132
Number of pages10
ISBN (Print)3540360182, 9783540360186
DOIs
StatePublished - Jan 1 2006
Event5th International Conference on Image and Video Retrieval, CIVR 2006 - Tempe, AZ, United States
Duration: Jul 13 2006Jul 15 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4071 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Image and Video Retrieval, CIVR 2006
CountryUnited States
CityTempe, AZ
Period7/13/067/15/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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