Article

Knowing a tree from the forest: art image retrieval using a society of profiles

Authors:

HongJiang Zhang,

Hans-Peter KriegelAuthors Info & Claims

MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on Multimedia

Pages 622 - 631

https://doi.org/10.1145/957013.957145

Published: 02 November 2003 Publication History

Abstract

This paper aims to address the problem of art image retrieval (AIR), which aims to help users find their favorite painting images. AIR is of great interests to us because of its application potentials and interesting research challenges---the retrieval is not only based on painting contents or styles, but also heavily based on user preference profiles. This paper describes the collaborative ensemble learning, a novel statistical learning approach to this task. It at first applies probabilistic support vector machines (SVMs) to model each individual user's profile based on given examples, i.e. liked or disliked paintings. Due to the high complexity of profile modelling, the SVMs can be rather weak in predicting preferences for new paintings. To overcome this problem, we combine a society of users' profiles, represented by their respective SVM models, to predict a given user's preferences for painting images. We demonstrate that the combination scheme is embedded in a Bayesian framework and retains intuitive interpretations---like-minded users are likely to share similar preferences. We report extensive empirical studies based on two experimental settings. The first one includes some controlled simulations performed on 4533 painting images. In the second setting, we report evaluations based on user preferences collected through an online web-based survey. Both experiments demonstrate that the proposed approach achieves excellent performance in terms of capturing a user's diverse preferences.

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Cited By

(2019)Visual Arts Search on Mobile DevicesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/332633615:2s(1-23)Online publication date: 3-Jul-2019
https://dl.acm.org/doi/10.1145/3326336
Mao HCheung MShe JLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)DeepArtProceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3123405(1183-1191)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3123405
Zhang RZhang Z(2007)FAST: Fast and Semantics-Tailored Image RetrievalMultimedia Data Mining and Knowledge Discovery10.1007/978-1-84628-799-2_8(141-167)Online publication date: 2007
https://doi.org/10.1007/978-1-84628-799-2_8
Show More Cited By

Index Terms

Knowing a tree from the forest: art image retrieval using a society of profiles
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on Multimedia

November 2003

670 pages

ISBN:1581137222

DOI:10.1145/957013

General Chairs:
Lawrence Rowe
University of California, Berkeley
,
Harrick Vin
University of Texas, Austin
,
Program Chairs:
Thomas Plagemann
University of Oslo
,
Prashant Shenoy
University of Massachusetts, Amherst
,
John R. Smith
IBM T.J. Watson Research Center

Copyright © 2003 ACM.

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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Published: 02 November 2003

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MM03: 2003 11th Annual ACM International Conference on Multimedia

November 2 - 8, 2003

CA, Berkeley, USA

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

(2019)Visual Arts Search on Mobile DevicesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/332633615:2s(1-23)Online publication date: 3-Jul-2019
https://dl.acm.org/doi/10.1145/3326336
Mao HCheung MShe JLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)DeepArtProceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3123405(1183-1191)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3123405
Zhang RZhang Z(2007)FAST: Fast and Semantics-Tailored Image RetrievalMultimedia Data Mining and Knowledge Discovery10.1007/978-1-84628-799-2_8(141-167)Online publication date: 2007
https://doi.org/10.1007/978-1-84628-799-2_8
Wang LLi QDong GLi Y(2007)Semantic knowledge facilities for a web‐based recipe database system supporting personalizationConcurrency and Computation: Practice and Experience10.1002/cpe.127520:7(753-782)Online publication date: 11-Oct-2007
https://doi.org/10.1002/cpe.1275
Zhang RSarukkai RChow JDai WZhang ZWang JBoujemaa NChen Y(2006)Joint categorization of queries and clips for web-based video searchProceedings of the 8th ACM international workshop on Multimedia information retrieval10.1145/1178677.1178705(193-202)Online publication date: 26-Oct-2006
https://dl.acm.org/doi/10.1145/1178677.1178705
Xie XLiu HGoumaz SMa WKellogg WZhai Svan der Veer GGale C(2005)Learning user interest for image browsing on small-form-factor devicesProceedings of the SIGCHI Conference on Human Factors in Computing Systems10.1145/1054972.1055065(671-680)Online publication date: 2-Apr-2005
https://dl.acm.org/doi/10.1145/1054972.1055065
Zhang RZhang Z(2004)A robust color object analysis approach to efficient image retrievalEURASIP Journal on Advances in Signal Processing10.1155/S111086570431214X2004(871-885)Online publication date: 1-Jan-2004
https://dl.acm.org/doi/10.1155/S111086570431214X

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