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An adaptive teleportation random walk model for learning social tag relevance

Published: 03 July 2014 Publication History

Abstract

Social tags are known to be a valuable source of information for image retrieval and organization. However, contrary to the conventional document retrieval, rich tag frequency information in social sharing systems, such as Flickr, is not available, thus we cannot directly use the tag frequency (analogous to the term frequency in a document) to represent the relevance of tags. Many heuristic approaches have been proposed to address this problem, among which the well-known neighbor voting based approaches are the most effective methods. The basic assumption of these methods is that a tag is considered as relevant to the visual content of a target image if this tag is also used to annotate the visual neighbor images of the target image by lots of different users. The main limitation of these approaches is that they treat the voting power of each neighbor image either equally or simply based on its visual similarity. In this paper, we cast the social tag relevance learning problem as an adaptive teleportation random walk process on the voting graph. In particular, we model the relationships among images by constructing a voting graph, and then propose an adaptive teleportation random walk, in which a confidence factor is introduced to control the teleportation probability, on the voting graph. Through this process, direct and indirect relationships among images can be explored to cooperatively estimate the tag relevance. To quantify the performance of our approach, we compare it with state-of-the-art methods on two publicly available datasets (NUS-WIDE and MIR Flickr). The results indicate that our method achieves substantial performance gains on these datasets.

References

[1]
S. F. Chang, T. Sikora, and A. Puri. Overview of the MPEG-7 standard. IEEE Trans. Circuits and Systems for Video Technology, 11(6):688--695, June 2001.
[2]
S. A. Chatzichristofis and Y. S. Boutalis. Fcth: Fuzzy color and texture histogram - a low level feature for accurate image retrieval. In Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS '08, pages 191--196, Washington, DC, USA, 2008. IEEE Computer Society.
[3]
T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng. Nus-wide: a real-world web image database from national university of singapore. In Proceedings of the ACM International Conference on Image and Video Retrieval, CIVR '09, pages 48:1--48:9, 2009.
[4]
N. Craswell and M. Szummer. Random walks on the click graph. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '07, pages 239--246, New York, NY, USA, 2007. ACM.
[5]
Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res., 4:933--969, Dec. 2003.
[6]
T. H. Haveliwala. Topic-sensitive pagerank. In Proceedings of the 11th international conference on World Wide Web, WWW '02, pages 517--526, New York, NY, USA, 2002. ACM.
[7]
M. J. Huiskes and M. S. Lew. The mir flickr retrieval evaluation. In Proceedings of the 1st ACM international conference on Multimedia information retrieval, MIR '08, pages 39--43, New York, NY, USA, 2008. ACM.
[8]
G. Jeh and J. Widom. Scaling personalized web search. In Proceedings of the 12th international conference on World Wide Web, WWW '03, pages 271--279, New York, NY, USA, 2003. ACM.
[9]
D. Jin, D. Liu, B. Yang, C. Baquero, and D. He. Ant colony optimization with markov random walk for community detection in graphs. In Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II, PAKDD'11, pages 123--134, Berlin, Heidelberg, 2011. Springer-Verlag.
[10]
Y. Jing and S. Baluja. Pagerank for product image search. In Proceedings of the 17th international conference on World Wide Web, WWW '08, pages 307--316, New York, NY, USA, 2008. ACM.
[11]
M. Li, B. M. Dias, I. Jarman, W. El-Deredy, and P. J. Lisboa. Grocery shopping recommendations based on basket-sensitive random walk. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 1215--1224, New York, NY, USA, 2009. ACM.
[12]
X. Li, C. G. Snoek, and M. Worring. Learning tag relevance by neighbor voting for social image retrieval. In Proceedings of the 1st ACM international conference on Multimedia information retrieval, MIR '08, pages 180--187, New York, NY, USA, 2008. ACM.
[13]
X. Li, C. G. M. Snoek, and M. Worring. Learning social tag relevance by neighbor voting. Trans. Multi., 11(7):1310--1322, Nov. 2009.
[14]
D. Liu, X.-S. Hua, L. Yang, M. Wang, and H.-J. Zhang. Tag ranking. In Proceedings of the 18th international conference on World wide web, WWW '09, pages 351--360, New York, NY, USA, 2009. ACM.
[15]
M. Lux and S. A. Chatzichristofis. Lire: lucene image retrieval: an extensible java cbir library. In Proceedings of the 16th ACM international conference on Multimedia, MM '08, pages 1085--1088, New York, NY, USA, 2008. ACM.
[16]
Q. Mei, J. Guo, and D. Radev. Divrank: The interplay of prestige and diversity in information networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '10, pages 1009--1018, New York, NY, USA, 2010. ACM.
[17]
K. Onuma, H. Tong, and C. Faloutsos. Tangent: A novel, 'surprise me', recommendation algorithm. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09, pages 657--666, New York, NY, USA, 2009. ACM.
[18]
L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. In Proceedings of the 7th International World Wide Web Conference, pages 161--172, 1998.
[19]
P. Pons and M. Latapy. Computing communities in large networks using random walks. In Proceedings of the 20th International Conference on Computer and Information Sciences, ISCIS'05, pages 284--293, Berlin, Heidelberg, 2005. Springer-Verlag.
[20]
A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell., 22(12):1349--1380, Dec. 2000.
[21]
A. Sun, S. S. Bhowmick, K. T. Nam Nguyen, and G. Bai. Tag-based social image retrieval: An empirical evaluation. J. Am. Soc. Inf. Sci. Technol., 62(12):2364--2381, Dec. 2011.
[22]
B. Q. Truong, A. Sun, and S. S. Bhowmick. Content is still king: the effect of neighbor voting schemes on tag relevance for social image retrieval. In Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, ICMR '12, pages 9:1--9:8, New York, NY, USA, 2012. ACM.
[23]
C. Wang, F. Jing, L. Zhang, and H.-j. Zhang. Scalable search-based image annotation. Multimedia Systems, 14(4):205--220, 2007.
[24]
J. Wang, J. Wang, G. Zeng, Z. Tu, R. Gan, and S. Li. Scalable k-nn graph construction for visual descriptors. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), CVPR '12, pages 1106--1113, 2012.
[25]
L. Wu, L. Yang, N. Yu, and X.-S. Hua. Learning to tag. In Proceedings of the 18th International Conference on World Wide Web, WWW '09, pages 361--370, New York, NY, USA, 2009. ACM.

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    cover image ACM Conferences
    SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
    July 2014
    1330 pages
    ISBN:9781450322577
    DOI:10.1145/2600428
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    Published: 03 July 2014

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    Author Tags

    1. neighbor voting
    2. random walk
    3. social tag relevance

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    SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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