Relevance Feedback Reinforced with Semantics Accumulation

Sangwook Oh, Min Gyo Chung, Sanghoon Sull

Research output: Contribution to journalArticle

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 Dec 1

Fingerprint

Relevance Feedback
Semantics
Feedback
Retrieval
Image Retrieval
Terminate
Accumulate
Query
Image retrieval
Experimental Results
Demonstrate
Relevance

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

@article{c3cc71de40254351b0ac5d35324a3c75,
title = "Relevance Feedback Reinforced with Semantics Accumulation",
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.",
author = "Sangwook Oh and Chung, {Min Gyo} and Sanghoon Sull",
year = "2004",
month = "12",
day = "1",
language = "English",
volume = "3115",
pages = "448--454",
journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Relevance Feedback Reinforced with Semantics Accumulation

AU - Oh, Sangwook

AU - Chung, Min Gyo

AU - Sull, Sanghoon

PY - 2004/12/1

Y1 - 2004/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=35048829944&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35048829944&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:35048829944

VL - 3115

SP - 448

EP - 454

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -