Abstract
Automatic image tagging automatically assigns image with semantic keywords called tags, which significantly facilitates image search and organization. Most of present image tagging approaches assign the query image with the tags derived from the visually similar images in the training dataset only. However, their scalabilities and performances are constrained by the limitation of using the training method and the fixed size tag vocabulary. In this paper, we proposed a search based probabilistic image tagging algorithm (CTSTag), in which the initially assigned tags are mined from the content-based search result and expanded from the text-based search results. Experiments on NUS-WIDE dataset show not only the performance of the proposed algorithm but also the advantage of image retrieval using the tagging result.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Joshi, D., Datta, R., Zhuang, Z., Weiss, W.P., Friedenberg, M., Li, J., Wang, J.Z.: PARAgrab: a comprehensive architecture for web image management and multimodal querying. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 1163–1166 (2006)
Zhang, B., Xiang, Q., Lu, H., Shen, J., Wang, Y.: Comprehensive query-dependent fusion using regression-on-folksonomies: a case study of multimodal music search. In: Proceedings of the 17th ACM International Conference on Multimedia, pp. 213–222 (2009)
Cui, B., Tung, A.K., Zhang, C., Zhao, Z.: Multiple feature fusion for social media applications. In: Proceedings of the International Conference on Management of Data, pp. 435–446 (2010)
Wang, C., Jing, F., Zhang, L., Zhang, H.-J.: Image annotation refinement using random walk with restarts. In: Proceedings of 14th ACM International Conference on Multimedia, pp. 647–650 (2006)
Siersdorfer, S., San Pedro, J., Sanderson, M.: Automatic Video Tagging using Content Redundancy. In: Proceeding of the 32nd ACM International Conference on Research and Development in Information Retrieval, pp. 16–23 (2009)
Zhou, X., Wang, M., Zhang, Q., Zhang, J., Shi, B.: Automatic image annotation by an iterative approach: Incorporating keyword correlations and region matching. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 25–32 (2007)
Liu, D., Wang, M., Hua, X.S., Zhang, H.J.: Tag Ranking. In: Proceeding of the 18th ACM International Conference on World Wide Web, pp. 351–340 (2009)
Li, X.R., Snoek, C.G.M., Worring, M.: Learning tag relevance by neighbor voting for social image retrieval. In: Proceeding of 1st ACM International Conference on Multimedia Information Retrieval, pp. 30–31 (2008)
Lei, W., Linjun, Y., Nenghai, Y., Hua, X.S.: Learning to tag. In: Proceedings of the 18th ACM International Conference on World Wide Web, pp. 20–24 (2009)
Lei, W., Steven, C.H., Rong Jin, H., Jianke, Z., Nenghai, Y.: Distance Metric Learning from Uncertain Side Information with Application to Automated Photo Tagging. In: Proceeding of 17th ACM International Conference on Multimedia, pp. 15–24 (2009)
Wang, X.-J., Zhang, L., Li, X.R., Ma, W.-Y.: Annotating Images by Mining Image Search Results. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 1919–1932 (2008)
Wang, C., Jing, F., Zhang, L., Zhang, H.J.: Scalable search-based image annotation. Multimedia Systems 14(4), 205–220 (2008)
Jing, F., Wang, C., Yao, Y., Deng, K., Zhang, L., Ma, W.: IGroup: web image search results clustering. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 377–384 (2006)
Tong, H., Faloutsos, C., Pan, J.: Fast Random Walk with Restart and Its Applications. In: Proceedings of the IEEE Sixth International Conference on Data Mining, pp. 613–622 (2006)
Croft, W.B., Lafferty, J.: Language Models for Information Retrieval. Kluwer Int. Series on Information Retrieval, vol. 13. Kluwer Academic Publishers, Dordrecht (2002)
Li, X., Snoek, C.G., Worring, M.: Learning social tag relevance by neighbor voting. IEEE Transaction on Multimedia, 1310–1322 (November 2009)
Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D.M., Jordan, M.I.: Matching words and pictures. Jour. Machine Learning Research 3(6), 1107–1135 (2003)
Geng, B., Yang, L., Xu, C., Hua, X.: Collaborative learning for image and video annotation. In: Proceeding of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 443–450 (2008)
Li, J., Wang, J.Z.: Real-time computerized annotation of pictures. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 911–920 (2006)
Jones, K.S., Walker, S., Robertson, S.E.: A probabilistic model of information retrieval: development and comparative experiments - part 2. Jour. Information Processing and Management 36(6), 809–840 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, X., Huang, Z., Shen, H.T., Li, Z. (2011). Probabilistic Image Tagging with Tags Expanded By Text-Based Search. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20149-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-20149-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20148-6
Online ISBN: 978-3-642-20149-3
eBook Packages: Computer ScienceComputer Science (R0)