Efficient Manifold Ranking for image retrieval

SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2011, Pages 525-534

  Xu, Bin ^a   Bu, Jiajun ^a   Chen, Chun ^a   Cai, Deng ^b   He, Xiaofei ^b   Liu, Wei ^c   Luo, Jiebo ^d  

^a ZHEJIANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c Och Spine at New York Presbyterian Hospitals   (United States)

^d EASTMAN KODAK COMPANY   (United States)

Author keywords

Efficient manifold ranking; Graph based algorithm; Image retrieval; Out of sample

Indexed keywords

CONTENT BASED RETRIEVAL; IMAGE RETRIEVAL; NEAREST NEIGHBOR SEARCH;

CONTENT BASED IMAGE RETRIEVAL; EFFECTIVENESS AND EFFICIENCIES; EFFICIENT COMPUTATION; GEOMETRICAL STRUCTURE; GRAPH BASED RANKINGS; GRAPH-BASED ALGORITHMS; K-NEAREST NEIGHBOR GRAPHS; MANIFOLD RANKING;

GRAPHIC METHODS;

EID: 80052136884     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2009916.2009988     Document Type: Conference Paper

References (37)

1. S. Agarwal. Ranking on graph data. In Proceedings of the 23rd International Conference on Machine Learning, pages 25-32, 2006.
2. S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Computer networks and ISDN systems, 30(1-7):107-117, 1998.
3. J. Bu, S. Tan, C. Chen, C. Wang, H. Wu, L. Zhang, and X. He. Music recommendation by unified hypergraph: combining social media information and music content. In Proceedings of the 18th annual ACM international conference on Multimedia, pages 391-400, 2010.
4. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
5. R. Datta, D. Joshi, J. Li, and J. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR), 40(2):1-60, 2008.
6. A. Frieze, R. Kannan, and S. Vempala. Fast Monte Carlo algorithms for finding low-rank approximations. Journal of the ACM (JACM), 51(6):1025-1041, 2004. (Pubitemid 40818314)
7. W. Gao, P. Cai, K. Wong, and A. Zhou. Learning to rank only using training data from related domain. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 162-169. ACM, 2010.
8. Z. Guan, J. Bu, Q. Mei, C. Chen, and C. Wang. Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 540-547, 2009.
9. J. He, M. Li, H. Zhang, H. Tong, and C. Zhang. Manifold-ranking based image retrieval. In Proceedings of the 12th annual ACM international conference on Multimedia, pages 9-16, 2004. (Pubitemid 40211712)
10. J. He, M. Li, H. Zhang, H. Tong, and C. Zhang. Generalized manifold-ranking-based image retrieval. IEEE Transactions on Image Processing, 15(10):3170-3177, 2006. (Pubitemid 44473655)
11. R. He, Y. Zhu, and W. Zhan. Fast Manifold-Ranking for Content-Based Image Retrieval. In ISECS International Colloquium on Computing, Communication, Control, and Management.
12. X. He, D. Cai, and J. Han. Learning a maximum margin subspace for image retrieval. IEEE Transactions on Knowledge and Data Engineering, pages 189-201, 2007.
13. J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 119-126, 2003.
14. T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu. An efficient k-means clustering algorithm: Analysis and implementation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):881-892, 2002. (Pubitemid 34835464)
15. J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5):604-632, 1999.
16. B. Li, E. Chang, and C. Wu. DPF - a perceptual distance function for image retrieval. In International Conference on Image Processing, volume 2, pages 597-600, 2002.
17. D. Liu, X. Hua, L. Yang, M. Wang, and H. Zhang. Tagranking. In Proceedings of the 18th international conference on World wide web, pages 351-360, 2009.
18. W. Liu, J. He, and S. Chang. Large graph construction for scalable semi-supervised learning. In Proceedings of the 27th International Conference on Machine Learning, pages 679-686, 2010.
19. Y. Liu, D. Zhang, G. Lu, and W. Ma. A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1):262-282, 2007. (Pubitemid 44486285)
20. D. Lowe. Object recognition from local scale-invariant features. In International Conference on Computer Vision, page 1150, 1999.
21. C. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval. Cambridge University Press, 2008.
22. J. Ponte and W. Croft. A language modeling approach to information retrieval. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 275-281. ACM, 1998.
23. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323, 2000.
24. Y. Rui, T. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool for interactive content-based image retrieval. Circuits and Systems for Video Technology, IEEE Transactions on, 8(5):644-655, 2002.
25. S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proceedings of the 9th ACM international conference on Multimedia, pages 107-118, 2001. (Pubitemid 32962460)
26. M. Tsai, T. Liu, T. Qin, H. Chen, and W. Ma. FRank: a ranking method with fidelity loss. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 383-390. ACM, 2007. (Pubitemid 350164984)
27. R. Veltkamp and M. Tanase. Content-Based Image Retrieval Systems: A Survey. 2002.
28. X. Wan, J. Yang, and J. Xiao. Manifold-ranking based topic-focused multi-document summarization. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, pages 2903-2908, 2007.
29. L. Wang, J. Lin, and D. Metzler. Learning to efficiently rank. In Proceeding of the 3 3rd international ACM SIGIR conference on Research and development in information retrieval, pages 138-145, 2010.
30. C. Williams and M. Seeger. Using the Nyström method to speed up kernel machines. In Advances in Neural Information Processing Systems 13, 2001.
31. H. Yu, M. Li, H. Zhang, and J. Feng. Color texture moments for content-based image retrieval. In International Conference on Image Processing, volume 3, pages 929-932, 2002.
32. K. Yu, J. Bi, and V. Tresp. Active learning via transductive experimental design. In Proceedings of the 23rd international conference on Machine learning, pages 1081-1088, 2006.
33. K. Yu, T. Zhang, and Y. Gong. Nonlinear learning using local coordinate coding. In Advances in Neural Information Processing Systems, 2009.
34. X. Yuan, X. Hua, M. Wang, and X. Wu. Manifold-ranking based video concept detection on large database and feature pool. In Proceedings of the 14th annual ACM International Conference on Multimedia, pages 623-626, 2006. (Pubitemid 47113592)
35. K. Zhang, J. Kwok, and B. Parvin. Prototype vector machine for large scale semi-supervised learning. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 1233-1240, 2009.
36. D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In Advances in Neural Information Processing Systems, pages 595-602, 2004.
37. D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Schölkopf. Ranking on data manifolds. In Advances in Neural Information Processing Systems, page 169, 2004.