research-article

Visual tag dictionary: interpreting tags with visual words

Authors:

Xian-Sheng Hua,

Hong-Jiang ZhangAuthors Info & Claims

WSMC '09: Proceedings of the 1st workshop on Web-scale multimedia corpus

Pages 1 - 8

https://doi.org/10.1145/1631135.1631137

Published: 23 October 2009 Publication History

Abstract

Visual-word based image representation has shown effectiveness in a wide variety of applications such as categorization, annotation and search. By detecting keypoints in images and treating their patterns as visual words, an image can be represented as a bag of visual words, which is analogous to the bag-of-words representation of text documents. In this paper, we introduce a corpus named visual tag dictionary. Unlike the conventional dictionaries that define terms with textual words, the visual tag dictionary interprets each tag with visual words. The dictionary is constructed in a fully automatic way by exploring the tagged image data on the Internet. With this dictionary, tags and images are connected via visual words and many applications can be thus facilitated. As examples, we empirically demonstrate the effectiveness of the dictionary in tag-based image search, tag ranking and image annotation.

References

[1]

C. M. D. Alamo, F. J. C. Gil, C. D. T. Munilla, and L. H. Gomez. Discriminative training of gmm for speaker identification. In Proceeding of ICASSP, 1996.

Digital Library

[2]

T. S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. T. Zheng. Nus-wide: A real-world web image database from national university of singapore. In Proceedings of ACM International Conference on Image and Video Retrieval, 2009.

Digital Library

[3]

K. Jarvelin and J. Kekalainen. Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information System, 2002.

Digital Library

[4]

F. Jing, L. Zhang, and W. Y. Ma. Visualtour: an online travel assistant based on high quality images. In Proceedings of ACM Multimedia, 2006.

Digital Library

[5]

L. S. Kennedy, S. F. Chang, and I. V. Kozintsev. To search or to label? predicting the performance of search-based automatic image classifiers. In Proceedings of ACM international workshop on Multimedia information retrieval, 2006.

Digital Library

[6]

R. H. V. Leuken, L. Garcia, X. Olivares, and R. Zwol. Visual diversification of image search results. In Proceedings of International World Wide Web Conference, 2009.

Digital Library

[7]

J. Li and J. Wang. Real-time computerized annotation of pictures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), 2008.

Digital Library

[8]

X. R. Li, C. G. Snoek, and M. Worring. Learning tag relevance by neighbor voting for social image retrieval. In Proceeding of ACM International Conference on Multimedia Information Retrieval, 2008.

Digital Library

[9]

D. Liu, X. S. Hua, L. Yang, M. Wang, and H. J. Zhang. Tag ranking. In Proceedings of International World Wide Web Conference, 2008.

Digital Library

[10]

D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 2004.

Digital Library

[11]

N. Naphade and J. R. Smith. The role of classifiers in multimedia content management. SPIE Storage and retrieval for media databases, 2003.

[12]

J. Rissanen. Stochastic complexity in statistical inquiry. World Scientific, 1989.

Digital Library

[13]

B. Sigurbjornsson and R. V. Zwol. Flickr tag recommendation based on collective knowledge. In Proceeding of ACM International World Wide Web Conference, 2008.

Digital Library

[14]

J. Tang, S. Yan, R. Hong, G. J. Qi, and T. S. Chua. Inferring semantic concepts from community-contributed images and noisy tags. In Proceeding of ACM Multimedia, 2009.

Digital Library

[15]

M. Wang, X. S. Hua, R. Hong, J. Tang, G. J. Qi, and Y. Song. Unified video annotation via multi-graph learning. IEEE Transactions on Circuits and Systems for Video Annotation, 19(5), 2009.

Digital Library

[16]

M. Wang, X. S. Hua, J. Tang, and R. Hong. Beyond distance measurement: constructing neighborhood similarity for video annotation. IEEE Transactions on Multimedia, 11(3), 2009.

Digital Library

[17]

K. Weinberger, M.Slaney, and R.V.Zowl. Resolving tag ambiguity. In Proceeding of ACM Multimedia, 2008.

Digital Library

[18]

L. Wu, X. S. Hua, N. Yu, W. Y. Ma, and S. Li. Flickr distance. In Proceeding of ACM Multimedia, 2008.

Digital Library

[19]

R. Yan, A. Natsev, and M. Campbell. A learning-based hybrid tagging and browsing approach for efficient manual image annotation. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, 2008.

[20]

A. Yanagawa, S.-F. Chang, L. Kennedy, and W. Hsu. Columbia university_Ss baseline detectors for 374 LSCOM semantic visual concepts. Columbia University ADVENT Technical Report 222-2006-8, 2007.

[21]

Q. Yang, X. Chen, and G. Wang. Web 2.0 dictionary. In Proceedings of ACM International Conference on Image and Video Retrieval, 2008.

Digital Library

Cited By

Wajid MZafar AWajid M(2023)A deep learning approach for image and text classification using neutrosophyInternational Journal of Information Technology10.1007/s41870-023-01529-816:2(853-859)Online publication date: 13-Oct-2023
https://doi.org/10.1007/s41870-023-01529-8
Iftikhar SJabeen FNasir HFida S(2020)WordNet and Wiki Based Approach for Finding Polysemy Tags in a Tag Set2020 International Conference on Intelligent Systems and Computer Vision (ISCV)10.1109/ISCV49265.2020.9204288(1-8)Online publication date: Jun-2020
https://doi.org/10.1109/ISCV49265.2020.9204288
Sun GWu XPeng Q(2016)Part-based clothing image annotation by visual neighbor retrievalNeurocomputing10.1016/j.neucom.2015.12.141213:C(115-124)Online publication date: 12-Nov-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.12.141
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Index Terms

Visual tag dictionary: interpreting tags with visual words
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Dictionaries

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Published In

cover image ACM Conferences

WSMC '09: Proceedings of the 1st workshop on Web-scale multimedia corpus

October 2009

56 pages

ISBN:9781605587615

DOI:10.1145/1631135

General Chairs:
Benoit Huet
EURECOM, France
,
Jinhui Tang
National University of Singapore, Singapore
,
Alex Hauptmann
Carnegie Mellon University, USA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2009

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MM09

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SIGMM

MM09: ACM Multimedia Conference

October 23, 2009

Beijing, China

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WSMC '09 Paper Acceptance Rate 12 of 20 submissions, 60%;

Overall Acceptance Rate 12 of 20 submissions, 60%

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Cited By

Wajid MZafar AWajid M(2023)A deep learning approach for image and text classification using neutrosophyInternational Journal of Information Technology10.1007/s41870-023-01529-816:2(853-859)Online publication date: 13-Oct-2023
https://doi.org/10.1007/s41870-023-01529-8
Iftikhar SJabeen FNasir HFida S(2020)WordNet and Wiki Based Approach for Finding Polysemy Tags in a Tag Set2020 International Conference on Intelligent Systems and Computer Vision (ISCV)10.1109/ISCV49265.2020.9204288(1-8)Online publication date: Jun-2020
https://doi.org/10.1109/ISCV49265.2020.9204288
Sun GWu XPeng Q(2016)Part-based clothing image annotation by visual neighbor retrievalNeurocomputing10.1016/j.neucom.2015.12.141213:C(115-124)Online publication date: 12-Nov-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.12.141
Liu XZhang JZhuo LYang Y(2015)Creating descriptive visual words for tag ranking of compressed social image2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7351536(3901-3905)Online publication date: Sep-2015
https://doi.org/10.1109/ICIP.2015.7351536
Zhang JLiu XZhuo LWang C(2015)Social images tag ranking based on visual words in compressed domainNeurocomputing10.1016/j.neucom.2014.11.027153(278-285)Online publication date: Apr-2015
https://doi.org/10.1016/j.neucom.2014.11.027
Li ZZhang JLiu XZhuo L(2014)Creating the Bag-of-Words with Spatial Context Information for Image RetrievalApplied Mechanics and Materials10.4028/www.scientific.net/AMM.556-562.4788556-562(4788-4791)Online publication date: May-2014
https://doi.org/10.4028/www.scientific.net/AMM.556-562.4788
Qian XHua XTang YMei T(2014)Social Image Tagging With Diverse SemanticsIEEE Transactions on Cybernetics10.1109/TCYB.2014.230959344:12(2493-2508)Online publication date: Dec-2014
https://doi.org/10.1109/TCYB.2014.2309593
Li ZZhang JLiu XZhuo L(2014)Efficient multi-nodes image retrieval method based on visual words2014 IEEE International Conference on Progress in Informatics and Computing10.1109/PIC.2014.6972324(200-203)Online publication date: May-2014
https://doi.org/10.1109/PIC.2014.6972324
Xie LHe XJaimes ASebe NBoujemaa NGatica-Perez DShamma DWorring MZimmermann R(2013)Picture tags and world knowledgeProceedings of the 21st ACM international conference on Multimedia10.1145/2502081.2502113(967-976)Online publication date: 21-Oct-2013
https://dl.acm.org/doi/10.1145/2502081.2502113
Hong RZha ZGao YChua TWu X(2013)Multimedia encyclopedia construction by mining web knowledgeSignal Processing10.1016/j.sigpro.2012.06.02893:8(2361-2368)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.1016/j.sigpro.2012.06.028
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