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Article

Web image retrieval on ImagEVAL: evidences on visualness and textualness concept dependency in fusion model

Authors:

Sabrina Tollari,

Hervé GlotinAuthors Info & Claims

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

Pages 65 - 72

https://doi.org/10.1145/1282280.1282289

Published: 09 July 2007 Publication History

Abstract

We present in this article an efficient visuo-textual Web Image Retrieval system (WIR), which is the second best system according to the official European ImagEVAL 2006 campaign evaluation. It uses very simple tfidf textual analysis, and subband entropy profile visual features. Our mean fusion model represents a simple but nearly state of the art WIR. We depict analyses of the fusion behavior of each query. We then demonstrate that "visualness" of images, and "textualness" of web page, relative to the discriminant power of each features, are concept dependant, and that fusion model could take advantage of their possible complementarity. We finally discuss on their automatic estimations that may enhance WIR.

References

[1]

K. Barnard and D. Forsyth. Learning the semantics of words and pictures. In International Conference on Computer Vision, volume 2, pages 408--415, 2001.

[2]

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6): 391--407, 1990.

[3]

H. Glotin, S. Tollari, and P. Giraudet. Shape reasoning on mis-segmented and mis-labeled objects using approximated fisher criterion. International Journal Computers and Graphics, 30(2), April 2006.

Digital Library

[4]

H. Glotin, D. Vergyri, C. Neti, G. Potamianos, and J. Luettin. Weighting schemes for audio-visual fusion in speech recognition. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Salt Lake City-USA, 2001.

[5]

T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1--2):177--196, 2001.

Digital Library

[6]

ImagEVAL. http://www.imageval.org.

[7]

ImagEVAL. Nicephore days: imagEVAL international results symposium, 2006. http://www.imageval.org.

[8]

M. La Cascia, S. Sethi, and S. Sclaroff. Combining textual and visual cues for content-based image retrieval on the world wide web. In IEEE Workshop on Content-based access of Image and Video, 1998.

Digital Library

[9]

V. Lavrenko, R. Manmatha, and J. Jeon. A model for learning the semantics of pictures. In Neural Information Processing Systems (NIPS), 2003.

[10]

J. Li and J. Z. Wang. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Pattern Analysis and Machine Intelligence, 25(9):1075--1088, 2003.

Digital Library

[11]

G. Salton and C. Buckley. Term-weighting approaches in automatic retrieval. Information processing and management, 24(5):513--523, 1988.

Digital Library

[12]

S. Sclaroff, L. Taycher, and M. L. Cascia. Imagerover: A content-based image browser for the world wide web. In Proceedings of IEEE Workshop on Content-based Access of Image and Video Libraries, 1997.

Digital Library

[13]

R. K. Srihari. Automatic indexing and content-based retrieval of captioned images. IEEE Computer, 28(9):49--56, 1995.

Digital Library

[14]

S. Tollari and H. Glotin. LDA versus MMD approximation on mislabeled images for keyword dependant selection of visual features and their heterogeneity. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2006.

[15]

S. Tollari, H. Glotin, and J. Le Maitre. Enhancement of textual images classification using segmented visual contents for image search engine. Multimedia Tools and Applications, 25(3):405--417, March 2005.

[16]

K. Yanai and K. Barnard. Image region entropy: a measure of "visualness" of web images associated with one concept. In ACM Multimedia, pages 419--422, 2005.

Digital Library

[17]

X. S. Zhou and T. S. Huang. Unifying keywords and visual contents in image retrieval. IEEE Multimedia, 9, 2002.

Digital Library

Cited By

Zhang XZhang XLi XLi ZWang S(2018)Classify social image by integrating multi-modal contentMultimedia Tools and Applications10.1007/s11042-017-4657-277:6(7469-7485)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11042-017-4657-2
Jiang JTong YLu HCui BLei KYu L(2017)GVoSACM Transactions on Information Systems10.1145/304165736:1(1-36)Online publication date: 5-Jun-2017
https://dl.acm.org/doi/10.1145/3041657
Liu CZhang XLi XLi RZhang XChao W(2016)Multi-modal learning for social image classification2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2016.7603345(1174-1179)Online publication date: Aug-2016
https://doi.org/10.1109/FSKD.2016.7603345
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Index Terms

Web image retrieval on ImagEVAL: evidences on visualness and textualness concept dependency in fusion model
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

July 2007

655 pages

ISBN:9781595937339

DOI:10.1145/1282280

General Chairs:
Nicu Sebe
Univ. of Amsterdam, The Netherlands
,
Marcel Worring
Univ. of Amsterdam, The Netherlands

Copyright © 2007 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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SIGMM: ACM Special Interest Group on Multimedia

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2007

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SIGMM

CIVR07: International Conference on Image and Video Retrieval 2007

July 9 - 11, 2007

Amsterdam, The Netherlands

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Cited By

Zhang XZhang XLi XLi ZWang S(2018)Classify social image by integrating multi-modal contentMultimedia Tools and Applications10.1007/s11042-017-4657-277:6(7469-7485)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11042-017-4657-2
Jiang JTong YLu HCui BLei KYu L(2017)GVoSACM Transactions on Information Systems10.1145/304165736:1(1-36)Online publication date: 5-Jun-2017
https://dl.acm.org/doi/10.1145/3041657
Liu CZhang XLi XLi RZhang XChao W(2016)Multi-modal learning for social image classification2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2016.7603345(1174-1179)Online publication date: Aug-2016
https://doi.org/10.1109/FSKD.2016.7603345
Moulin CLargeron CDucottet CGéry MBarat C(2014)Fisher Linear Discriminant Analysis for text-image combination in multimedia information retrievalPattern Recognition10.1016/j.patcog.2013.06.00347:1(260-269)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1016/j.patcog.2013.06.003
Song JYang YHuang ZShen HLuo J(2013)Effective Multiple Feature Hashing for Large-Scale Near-Duplicate Video RetrievalIEEE Transactions on Multimedia10.1109/TMM.2013.227174615:8(1997-2008)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.1109/TMM.2013.2271746
Tirilly PMu XHuang CXie IJeong WZhang JKamps JKraaij WFuhr N(2012)On the consistency and features of image similarityProceedings of the 4th Information Interaction in Context Symposium10.1145/2362724.2362754(164-173)Online publication date: 21-Aug-2012
https://dl.acm.org/doi/10.1145/2362724.2362754
Buffoni DTollari SGallinari P(2012)A Learning to Rank framework applied to text-image retrievalMultimedia Tools and Applications10.1007/s11042-011-0806-160:1(161-180)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1007/s11042-011-0806-1
Buffoni DTollari SGallinari P(2011)The importance of the depth for text-image selection strategy in learning-to-rankProceedings of the 33rd European conference on Advances in information retrieval10.5555/1996889.1996992(743-746)Online publication date: 18-Apr-2011
https://dl.acm.org/doi/10.5555/1996889.1996992
Song JYang YHuang ZShen HHong RCandan KPanchanathan SPrabhakaran BSundaram HFeng WSebe N(2011)Multiple feature hashing for real-time large scale near-duplicate video retrievalProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072354(423-432)Online publication date: 28-Nov-2011
https://dl.acm.org/doi/10.1145/2072298.2072354
Tirilly PLu KMu XZhao TCao Y(2011)On modality classification and its use in text-based image retrieval in medical databases2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI)10.1109/CBMI.2011.5972530(109-114)Online publication date: Jun-2011
https://doi.org/10.1109/CBMI.2011.5972530
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