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Leveraging Active Learning for Relevance Feedback Using an Information Theoretic Diversity Measure

  • Charlie K. Dagli
  • Shyamsundar Rajaram
  • Thomas S. Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)

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.

Keywords

Image Retrieval Relevance Feedback Query Point Entropic Diversity Query Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Charlie K. Dagli
    • 1
  • Shyamsundar Rajaram
    • 1
  • Thomas S. Huang
    • 1
  1. 1.Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbana

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