Abstract
With the recent rapid growth of social image hosting websites, such as Flickr, it is easier to construct a large database with tagged images. Social tags have been proven to be effective for providing keyword-based image retrieval and widely used on these websites, but whether they are beneficial for improving content-based image retrieval has not been well investigated in previous work. In this paper, we investigate whether and how social tags can be used for improving content-based image search results. We propose an unsupervised approach for automatic ranking without user interactions. It propagates visual and textual information on an image-tag relationship graph with a mutual reinforcement process. We conduct experiments showing that our approach can successfully use social tags for ranking and improving content-based social image search results, and performs better than other approaches.
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.
Similar content being viewed by others
References
Bischoff, K., Firan, C.S., Nejdl, W., Paiu, R.: Can all tags be used for search? In: CIKM, pp. 193–202 (2008)
Lin, W.H., Jin, R., Hauptmann, A.: Web Image Retrieval Re-Ranking with Relevance Model. In: IEEE/WIC, pp. 242–248 (2003)
Zitouni, H., Sevil, S., Ozkan, D., Duygulu, P.: Re-ranking of web image search results using a graph algorithm. In: ICPR, pp. 1–4 (2008)
Zhou, W.G., Tian, Q., Yang, L.J., Li, H.Q.: Latent visual context analysis for image re-ranking. In: CIVR, pp. 205–212 (2010)
Jing, Y., Baluja, S.: VisualRank: Applying PageRank to Large-Scale Image Search. TPAMI 30(11), 1877–1890 (2008)
Yong, R., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. CSVT 8(5), 644–655 (1998)
Porkaew, K., Mehrotra, S., Ortega, M.: Query reformulation for content based multimedia retrieval in MARS. In: ICMCS, pp. 747–751 (1999)
Porkaew, K., Chakrabarti, K., Mehrotra, S.: Query Refinement for Multimedia Similarity Retrieval in MARS. ACM Multimedia, 235–238 (1999)
Zhang, L., Lin, F.Z., Zhang, B.: Support vector machine learning for image retrieval. In: ICIP, pp. 721–724 (2001)
Chen, Y.Q., Zhou, X.S., Huang, T.S.: One-class SVM for learning in image retrieval. In: ICIP, pp. 34–37 (2001)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating Color Descriptors for Object and Scene Recognition. TPAMI 32(9), 1582–1596 (2010)
Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: WWW, pp. 327–336 (2008)
Chua, T.S., Tang, J.H., Hong, R.C., Li, H.J., Luo, Z.P., Zheng, Y.T.: NUS-WIDE: A Real-World Web Image Database from National University of Singapore. In: CIVR (2009)
Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20(4) (2001)
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
Li, J., Ma, Q., Asano, Y., Yoshikawa, M. (2011). Ranking Content-Based Social Images Search Results with Social Tags. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-25631-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25630-1
Online ISBN: 978-3-642-25631-8
eBook Packages: Computer ScienceComputer Science (R0)