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Reseach Article

Predictive Tagging of Social Media Images using Unsupervised Learning

by Nishchol Mishra, Sanjay Silakari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 24
Year of Publication: 2013
Authors: Nishchol Mishra, Sanjay Silakari
10.5120/11280-6563

Nishchol Mishra, Sanjay Silakari . Predictive Tagging of Social Media Images using Unsupervised Learning. International Journal of Computer Applications. 65, 24 ( March 2013), 46-52. DOI=10.5120/11280-6563

@article{ 10.5120/11280-6563,
author = { Nishchol Mishra, Sanjay Silakari },
title = { Predictive Tagging of Social Media Images using Unsupervised Learning },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 24 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 46-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number24/11280-6563/ },
doi = { 10.5120/11280-6563 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:20:50.391640+05:30
%A Nishchol Mishra
%A Sanjay Silakari
%T Predictive Tagging of Social Media Images using Unsupervised Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 24
%P 46-52
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The popularity of online social media has provided a huge repository of multimedia contents. To effectively retrieve and store this multimedia content and to mine useful pattern from this data is a herculean task. This paper deals with the problems of social image tagging. Multimedia tagging i.e. assigning tags or some keywords to multimedia contents like images, audio, video etc. by users is reshaping the way the people generally search multimedia resources. This huge amount of data must be effectively mined and knowledge is discovered to find some useful patterns hidden in it. Some facts like Facebook which has more than one billion active users, and millions of photos are uploaded daily, YouTube has 490 million unique users who visit every month, People upload 3,000 images to Flickr (the photo sharing social media site) every minute, Flickr hosts over 5 billion images. Apart from their usage for general purpose search, they are also leading towards many diverse areas of research like land mark recognition, tag recommendation, tag relevancy, automatic image tagging or annotation. This paper addresses the problem of automatic image tagging or Predictive tagging of digital images in social network scenario. Predictive tagging aims to automatically predict tags and check the relevancy of tags associated with images. This can be accomplished by using unsupervised learning.

References
  1. C. Wang, F. Jing, L. Zhang, and H.-J.Zhang, “Scalable search-based image annotation,” Multimedia Systems, vol. 14, no. 4, pp. 205–220, 2008.
  2. A. Torralba, R. Fergus, and W. T. Freeman, “80 million tiny images: A large data set for nonparametric object and scene recognition,” IEEE Trans. PAMI, vol. 30, no. 11, pp. 1958–1970, 2008.
  3. K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. M. Blei, and M. I. Jordan, “Matching words and pictures,” Jour. Machine Learning Research, vol. 3, no. 6, pp. 1107–1135, 2003.
  4. R. Datta, D. Joshi, J. Ali, J. Z. Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age”, ACM Computing Surveys, Vol. 40, No. 2, Article 5, 2008, pp. 5:1-5:59.
  5. Arnold W.M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain, “Content-Based Image Retrieval at the End of the Early Years”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, 2000, pp.1349-1380.
  6. Xirong Li, Cees G.M. Snoek, and Marcel Worring, “Learning Social Tag Relevance by Neighbor Voting”, in IEEE Transactions on Multimedia , volume 11, issue 7, page 1310-1322, 2009.
  7. J. Li and J. Z. Wang, “Real-time computerized annotation of pictures,” IEEE Trans. PAMI, vol. 30, no. 6, pp. 985–1002, 2008.
  8. C. Cusano, G. Ciocca, and R. Schettini, “Image annotation using SVM,” in Proc. SPIE, 2004, pp. 330–338.
  9. Y. Jin, L. Khan, L. Wang, and M. Awad, “Image annotations by combining multiple evidence &Wordnet,” in Proc. ACM Multimedia, 2005, pp. 706–715.
  10. B. Sigurbjornsson and R. van Zwol, “Flickr tag recommendation based on collective knowledge,” in Proc. WWW, 2008, pp. 327–336.
  11. K. Weinberger, M. Slaney, and R. van Zwol, “Resolving tag ambiguity,” in Proc. ACM Multimedia, 2008, pp. 111–119.
  12. Xirong Li, Cees G.M. Snoek, and Marcel Worring, “Learning Tag Relevance by Neighbor Voting for Social Image Retrieval”, in Proceedings of the ACM International Conference on Multimedia Information Retrieval (MIR), Vancouver, Canada, October, 2008.
  13. Li, Xirong, Cees GM Snoek, and Marcel Worring. "Unsupervised multi-feature tag relevance learning for social image retrieval." In Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 10-17. ACM, 2010.
  14. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vision, vol. 60, no. 2, pp. 91–110, 2004.
  15. Long, Fuhui, Hongjiang Zhang, and David Dagan Feng. "Fundamentals of content-based image retrieval." Multimedia Information Retrieval and Management 17 (2003): 1-26.
Index Terms

Computer Science
Information Sciences

Keywords

Predictive tagging tag relevancy image retrieval social media analytics