poster

Exploring Flickr's related tags for semantic annotation of web images

Authors:

Xiangdong Zhou,

Baile ShiAuthors Info & Claims

CIVR '09: Proceedings of the ACM International Conference on Image and Video Retrieval

Article No.: 46, Pages 1 - 8

https://doi.org/10.1145/1646396.1646450

Published: 08 July 2009 Publication History

Abstract

Exploring social media resources, such as Flickr and Wikipedia to mitigate the difficulty of semantic gap has attracted much attention from both academia and industry. In this paper, we first propose a novel approach to derive semantic correlation matrix from Flickr's related tags resource. We then develop a novel conditional random field model for Web image annotation, which integrates the keyword correlations derived from Flickr, and the textual and visual features of Web images into an unified graph model to improve the annotation performance. The experimental results on real Web image data set demonstrate the effectiveness of the proposed keyword correlation matrix and the Web image annotation approach.

References

[1]

http://www.flickr.net.

[2]

E. Chang and et al. Cbsa: Content-based soft annotation for multimodal image retrieval using bayes point machines. CirSys Video, 13(1): 26--38, 2003.

Digital Library

[3]

F. Christiane. Wordnet: An electronic lexical database. MIT press, 1998.

[4]

P. Duygulu, K. Barnard, J. de Freitas, and D. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. ECCV, pages 97--112, 2002.

Digital Library

[5]

H. Feng, R. Shi, and T.-S. Chua. A bootstrapping framework for annotating and retrieving www images. ACM Multimedia, pages 960--967, 2004.

Digital Library

[6]

S. Feng and R. Manmatha. A discrete direct retrieval model for image and video retrieval. CIVR, 2008.

Digital Library

[7]

S. Feng, R. Manmatha, and V. Lavrenko. Multiple bernoulli relevance models for image and video annotation. CVPR, pages 1002--1009, 2004.

Digital Library

[8]

J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. SIGIR, pages 119--126, 2003.

Digital Library

[9]

R. Jin, J. Chai, and L. Si. Effective automatic image annotation via a coherent language model and active learning. ACM Multimedia, 2004.

Digital Library

[10]

Y. Jin, L. Khan, L. Wang, and M. Awad. Image annotations by combining multiple evidence&wordnet. ACM Multimedia, pages 706--715, 2005.

Digital Library

[11]

J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML, 2001.

Digital Library

[12]

V. Lavrenko, R. Manmatha, and J. Jeon. A model for learning the semantics of pictures. NIPS, 2003.

[13]

M. S. Lew, N. Sebe, C. Djeraba, and R. Jain. Content-based multimedia information retrieval: State of the art and challenges. TOMCCAP, pages 1--19, 2006.

Digital Library

[14]

X. Li, L. Chen, L. Zhang, F. Lin, and W. Ma. Image annotation by large-scale content-based image retrieval. ACM Multimedia, pages 607--610, 2006.

Digital Library

[15]

J. Liu, M. Li, W. Ma, Q. Liu, and H. Lu. An adaptive graph model for automatic image annotation. ACM Multimedia, 2006.

Digital Library

[16]

R. Pedersen, S. Patwardhan, and J. Michelizzi. Wordnet::similarity - measuring the relatedness of concepts. AAAI, 2004.

Digital Library

[17]

B. Ricardo and R. Berthier. Modern information retrieval. New York: ACM Press, 1999.

Digital Library

[18]

H. Sanderson and M. Dunlop. Image retrieval by hypertext links. SIGIR, pages 296--303, 1997.

Digital Library

[19]

P. Schmitz. Inducing ontology from flickr tags. WWW, 2006.

[20]

Y. Song, Z. Zhuang, H. Li, and Q. Zhao. Real-time automatic tag recommendation. SIGIR, pages 515--522, 2008.

Digital Library

[21]

M. Srikanth, J. Varner, M. Bowden, and D. Moldovan. Exploiting ontologies for automatic image annotation. SIGIR, pages 552--558, 2005.

Digital Library

[22]

J. Tang, X.-S. Hua, G.-J. Qi, M. Wang, T. Mei, and X. Wu. Structure-sensitive manifold ranking for video concept detection. ACM Multimedia, 2007.

Digital Library

[23]

J. Tang and P. Lewis. Using multiple segmentations for image auto-annotation. CIVR, pages 581--586, 2007.

Digital Library

[24]

V. Tseng, J. Su, B. Wang, and Y. Lin. Web image annotation by fusing visual features and textual information. SAC, pages 1056--1060, 2007.

Digital Library

[25]

B. Wang, Z. Li, N. Yu, and M. Li. Image annotation in a progressive way. ICME, pages 1483--1490, 2007.

[26]

P. Wang and C. Domeniconi. Building semantic kernels for text classification using wikipedia. KDD, 2008.

Digital Library

[27]

L. Wu, X.-S. Hua, N. Yu, W.-Y. Ma, and S. Li. Flickr distance. ACM MM, 2008.

Digital Library

[28]

H. Xu, X. Zhou, and L. Lin. Wisa: A novel web image semantic analysis system. SIGIR, 2008.

Digital Library

[29]

C. Yang and M. Dong. Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning. CVPR, pages 2057--2063, 2006.

Digital Library

[30]

X. Zhou, M. Wang, Q. Zhang, J. Zhang, and B. Shi. Automatic image annotation by an iterative approach: incorporating keyword correlations and region matching. CIVR, pages 25--32, 2007.

Digital Library

Cited By

Bouchakwa MAyadi YAmous I(2020)A review on visual content-based and users’ tags-based image annotation: methods and techniquesMultimedia Tools and Applications10.1007/s11042-020-08862-1Online publication date: 9-May-2020
https://doi.org/10.1007/s11042-020-08862-1
Katsurai MOgawa THaseyama M(2018)A Cross-Modal Approach for Extracting Semantic Relationships Between Concepts Using Tagged ImagesIEEE Transactions on Multimedia10.1109/TMM.2014.230665516:4(1059-1074)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1109/TMM.2014.2306655
Sabino ARodrigues AGoulão MGouveia J(2014)Indirect Keyword RecommendationProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0110.1109/WI-IAT.2014.60(384-391)Online publication date: 11-Aug-2014
https://dl.acm.org/doi/10.1109/WI-IAT.2014.60
Show More Cited By

Index Terms

Exploring Flickr's related tags for semantic annotation of web images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Conferences

CIVR '09: Proceedings of the ACM International Conference on Image and Video Retrieval

July 2009

383 pages

ISBN:9781605584805

DOI:10.1145/1646396

Conference Chairs:
Yiannis Kompatsiaris
CERTH-ITI, Greece
,
Stephane Marchand-Maillet
Univ. of Geneva, Switzerland
,
Program Chairs:
Yannis Avrithis
NTUA, Greece
,
Noel O Connor
DCU, Ireland
,
Daniel Gatica-Perez
Idiap Research Institute, Switzerland
,
Tat-Seng Chua
National University of Singapore, Singapore

Copyright © 2009 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: 08 July 2009

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National Natural Science Foundation of China

Conference

CIVR '09

Sponsor:

SIGMM

CIVR '09: CIVR '09 - International Conference on Image and Video Retrieval

July 8 - 10, 2009

Santorini, Fira, Greece

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Citations

Cited By

Bouchakwa MAyadi YAmous I(2020)A review on visual content-based and users’ tags-based image annotation: methods and techniquesMultimedia Tools and Applications10.1007/s11042-020-08862-1Online publication date: 9-May-2020
https://doi.org/10.1007/s11042-020-08862-1
Katsurai MOgawa THaseyama M(2018)A Cross-Modal Approach for Extracting Semantic Relationships Between Concepts Using Tagged ImagesIEEE Transactions on Multimedia10.1109/TMM.2014.230665516:4(1059-1074)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1109/TMM.2014.2306655
Sabino ARodrigues AGoulão MGouveia J(2014)Indirect Keyword RecommendationProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0110.1109/WI-IAT.2014.60(384-391)Online publication date: 11-Aug-2014
https://dl.acm.org/doi/10.1109/WI-IAT.2014.60
Nack FScherp ANeuhaus C(2014)Semiotic TaggingProceedings of the 2014 IEEE International Conference on Semantic Computing10.1109/ICSC.2014.10(7-14)Online publication date: 16-Jun-2014
https://dl.acm.org/doi/10.1109/ICSC.2014.10
Zhang XYang YHan ZWang HGao C(2013)Object class detectionACM Computing Surveys (CSUR)10.1145/2522968.252297846:1(1-53)Online publication date: 11-Jul-2013
https://dl.acm.org/doi/10.1145/2522968.2522978
Feki GKsibi ABen Ammar ABen Amar C(2013)Improving image search effectiveness by integrating contextual information2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)10.1109/CBMI.2013.6576573(149-154)Online publication date: Jun-2013
https://doi.org/10.1109/CBMI.2013.6576573
Ksibi ABen Ammar ABen Amar C(2013)Enhanced context-based query-to-concept mapping in social image retrieval2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)10.1109/CBMI.2013.6576559(85-89)Online publication date: Jun-2013
https://doi.org/10.1109/CBMI.2013.6576559
Ewerth RBallafkir KMuhling MSeiler DFreisleben B(2012)Long-Term Incremental Web-Supervised Learning of Visual Concepts via Random SavannasIEEE Transactions on Multimedia10.1109/TMM.2012.218695614:4(1008-1020)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1109/TMM.2012.2186956
Ksibi ADammak MBen Ammar AMejdoub MBen Amar C(2012)Flickr-based semantic context to refine automatic photo annotation2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)10.1109/IPTA.2012.6469550(377-382)Online publication date: Oct-2012
https://doi.org/10.1109/IPTA.2012.6469550
Ksibi ABen Ammar ABen Amar C(2012)Effective concept detection using Second order Co-occurence Flickr context similarity measure SOCFCS2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)10.1109/CBMI.2012.6269846(1-6)Online publication date: Jun-2012
https://doi.org/10.1109/CBMI.2012.6269846
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