A novel approach to auto image annotation based on pairwise constrained clustering and Semi-Na&ve Bayesian model

Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005

Volumn , Issue , 2005, Pages 322-327

  Rui, Shi ^a   Jin, Wanjun ^a,b   Chua, Tat Seng ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b FUDAN UNIVERSITY   (China)

Author keywords

Image annotation; Pair wise constraint; Semi na ve Bayes; Semi supervised clustering

Indexed keywords

AUTOMATIC IMAGE ANNOTATION; CONSTRAINED CLUSTERING; CONTENT BASED IMAGE RETRIEVAL; IMAGE ANNOTATION; INDEPENDENCE ASSUMPTION; PAIRWISE CONSTRAINTS; SEMI-SUPERVISED CLUSTERING; STATE-OF-THE-ART TECHNIQUES;

BAYESIAN NETWORKS; CLUSTERING ALGORITHMS; COMPUTATIONAL LINGUISTICS; CONTENT BASED RETRIEVAL; IMAGE ANALYSIS;

IMAGE SEGMENTATION;

EID: 84871610398     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MMMC.2005.14     Document Type: Conference Paper

References (26)

1. Y. A. Aslandogan and C. T. Yu, "Multiple evidence combination in image retrieval: Diohenese searches for people on the web," ACM SIGIR'2000, Athens, Greece, 2000.
2. K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei & M.I. Jordan, "Matching words and pictures," Journal of Machine Learning Research, 3,:1107-1135, 2003.
3. M. Bilenko, S. Basu & R.J. Mooney, "Integrating constraints and metric learning in semi-supervised clustering," to appear in the Proc. of the 21st Int. Conf. on Machine Learning (ICML-2004), Banff, Canada, July 2004.
4. D. Blei & M.I. Jordan, "Modeling annotated data," Proc. of ACM SIGIR, 127-134. ACM Press, 2003.
5. C. Carson, M. Thomas, S. Belongie, J.M. Hellerstein and J. Malik, "Blobworld: A system for region-based image indexing and retrieval," Proc. Int'l Conf. Visual Information System, 1999.
6. J. Jeon, V. Lavrenko and R. Manmatha, "Automatic image annotation and retrieval using cross-media relevance models," ACM SIGIR'03, July 28-Aug 1, 2003.
7. I. Kononenko, "Semi-naïve Bayesian classifier," Sixth European Working Session on Learning. 206-219. 1991.
8. V. Lavrenko, R. Manmatha & J. Jeon, "A model for learning the semantics of pictures," Neural Information Processing System (NIPS), 2003.
9. J.M. Liu & T.-S. Chua, "Building semantic perceptron net for topic spotting," 39th Annual Meeting of Association for Computational Linguistic (ACL 2001), 370-377, 2001.
10. B. Manjunath and W. Ma, "Texture features for browsing and retrieval of image data," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-842, Aug 1996.
11. T.M. Mitchell, "Machine Learning," McGraw Hill, 1997.
12. Y. Mori, H. Takahashi and R. Oka, "Image-to-word transformation based on dividing and vector quantizing images with words," In Proc. of First International Journal of Computer Vision, 40(2): 99-121, 2000.
13. M.J. Pazzani, "Searching dependency in Bayesian classifiers," Learning from data: Artificial intelligence and statistics V, New York, NY: Springer-Verlag, editor D. Fisher and H.-J. Lenz, pp. 239-248, 1996.
14. J.C. Platt, "Probabilities for SV machines," In A. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 61-74, Cambridge, MA, 1999, MIT Press.
15. K. Rodden, "How do people organize their photographs?" In BCS IRSG 21st Ann. Colloq. on Info. Retrieval Research, 1999.
16. J.R. Smith and S.F. Chang, "VisualSeek: A fully automated content-based query system," ACM Multimedia, 1996.
17. J.R. Smith and C.S. Li, "Image classification and querying using composite region templates," Journal of Computer Vision and Image Understanding, 2000.
18. M. Swain and D. Ballard, "Color indexing," International Journal of Computer Vision, vol. 7, no. 1, pp. 11-32, 1991.
19. M. Szummer and R.W. Picard, "Indoor-outdoor image classification," IEEE Intl Workshop on Content-based Access of Image and Video Databases, Jan 1998.
20. R. Shi, H.M. Feng, T.-S. Chua & C.-H. Lee, "An adaptive image content representation and segmentation approach to automatic image annotation," Int'l Conf. on Image and Video Retrieval, July 21-23, 2004.
21. V. Vapnik, "The Nature of Statistical Learning Theory," Springer, New York, 1995.
22. K. Wagstaff, C. Cardie, S. Rogers & S. Schroedl, "Constrained K-means clustering with background knowledge," Proc. of Int'l Conference on Machine Learning (ICML-2001).
23. J.Z. Wang and J. Li, "Learning-based linguistic indexing of pictures with 2-D MHMMs," Proc. ACM Multimedia, pp. 436-445, Juan Les Pins, France, Dec 2002.
24. R. Yan & A. Hauptman, "A discriminative learning framework with pair-wise constraints for video object classification," CVPR 2004.
25. http://www.loc.gov/rr/print/tgm1/.
26. http://bach.ece.jhu.edu/svm/ginisvm/