Article

Scalable relevance feedback using click-through data for web image retrieval

Authors:

En Cheng,

Feng Jing,

Lei Zhang,

Hai JinAuthors Info & Claims

MM '06: Proceedings of the 14th ACM international conference on Multimedia

Pages 173 - 176

https://doi.org/10.1145/1180639.1180688

Published: 23 October 2006 Publication History

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Abstract

Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalability, efficiency and effectiveness issues. In this paper we proposed a scalable relevance feedback mechanism using click-through data for web image retrieval. The proposed mechanism regards users' click-through data as implicit feedback which could be collected at lower cost, in larger quantities and without extra burden on the user. During RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Experimental results on a database consisting of nearly three million Web images show that the proposed mechanism is wieldy, scalable and effective.

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Cited By

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Yanai K(2015)[Invited Paper] A Review of Web Image MiningITE Transactions on Media Technology and Applications10.3169/mta.3.1563:3(156-169)Online publication date: 2015
https://doi.org/10.3169/mta.3.156
Papadopoulos GApostolakis KDaras P(2014)Gaze-Based Relevance Feedback for Realizing Region-Based Image RetrievalIEEE Transactions on Multimedia10.1109/TMM.2013.229153516:2(440-454)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.1109/TMM.2013.2291535
Xiang J(2012)A Novel Retrieval Method Based on Semantic MotionAdvanced Materials Research10.4028/www.scientific.net/AMR.468-471.103468-471(103-106)Online publication date: Feb-2012
https://doi.org/10.4028/www.scientific.net/AMR.468-471.103
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Index Terms

Scalable relevance feedback using click-through data for web image retrieval
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
  2. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

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Information

Published In

MM '06: Proceedings of the 14th ACM international conference on Multimedia

October 2006

1072 pages

ISBN:1595934472

DOI:10.1145/1180639

General Chairs:
Klara Nahrstedt
UIUC
,
Matthew Turk
UCSB
,
Program Chairs:
Yong Rui
Microsoft Research
,
Wolfgang Klas
Universität Wien
,
Ketan Mayer-Patel
UNC

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 23 October 2006

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MM06: The 14th ACM International Conference on Multimedia 2006

October 23 - 27, 2006

CA, Santa Barbara, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Yanai K(2015)[Invited Paper] A Review of Web Image MiningITE Transactions on Media Technology and Applications10.3169/mta.3.1563:3(156-169)Online publication date: 2015
https://doi.org/10.3169/mta.3.156
Papadopoulos GApostolakis KDaras P(2014)Gaze-Based Relevance Feedback for Realizing Region-Based Image RetrievalIEEE Transactions on Multimedia10.1109/TMM.2013.229153516:2(440-454)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.1109/TMM.2013.2291535
Xiang J(2012)A Novel Retrieval Method Based on Semantic MotionAdvanced Materials Research10.4028/www.scientific.net/AMR.468-471.103468-471(103-106)Online publication date: Feb-2012
https://doi.org/10.4028/www.scientific.net/AMR.468-471.103
Smith GBrien CAshman H(2012)Evaluating implicit judgments from image search clickthrough dataJournal of the American Society for Information Science and Technology10.1002/asi.2274263:12(2451-2462)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1002/asi.22742
Murdock Vvan Zwol RGarcia LOlivares X(2010)Image Retrieval in a Commercial SettingImageCLEF10.1007/978-3-642-15181-1_26(483-505)Online publication date: 2010
https://doi.org/10.1007/978-3-642-15181-1_26
Cheng EJing FZhang CZhang L(2007)Search Result Clustering Based Relevance Feedback for Web Image Retrival2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '0710.1109/ICASSP.2007.366069(I-961-I-964)Online publication date: Apr-2007
https://doi.org/10.1109/ICASSP.2007.366069

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Click data as implicit relevance feedback in web search

A unified relevance feedback framework for web image retrieval

Web image retrieval reranking with multi-view clustering

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