Relevance Feedback Reinforced with Semantics Accumulation

Sangwook Oh, Min Gyo Chung, Sanghoon Sull

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Relevance feedback (RF) is a mechanism introduced earlier to exploit a user's perceptual feedback in image retrieval. It refines a query by using the relevance information from the user to improve subsequent retrieval. However, the user's feedback information is generally lost after a search session terminates. In this paper, we propose an enhanced version of RF, which is designed to accumulate human perceptual responses over time through relevance feedback and to dynamically combine the accumulated high-level relevance information with low-level features to further improve the retrieval effectiveness. Experimental results are presented to demonstrate the potential of the proposed method.

Original languageEnglish
Pages (from-to)448-454
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3115
Publication statusPublished - 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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