Relevance Feedback Reinforced with Semantics Accumulation

Sangwook Oh, Min Gyo Chung, Sanghoon Sull

    Research output: Contribution to journalArticlepeer-review

    3 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
    • General Computer Science

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