Multi-modal image annotation with Multi-instance Multi-label LDA

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 1558-1564

  Nguyen, Cam Tu ^a   Zhan, De Chuan ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ANNOTATION; IMAGE REGIONS; INPUT PATTERNS; LATENT DIRICHLET ALLOCATION; MULTI-MODAL; MULTI-MODAL IMAGE; TEXTUAL INFORMATION; VISUAL INFORMATION;

ARTIFICIAL INTELLIGENCE; SEMANTICS; STATISTICS;

IMAGE ANALYSIS;

EID: 84896063590     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. P. K. Atrey, M. A. Hossain, A. El- Saddik, and M. S. Kankanhalli. Multimodal fusion for multimedia analysis: A survey. Multimedia Systems, 16(6):345-379, 2010.
2. D. Blei and M. I. Jordan. Modeling annotated data. In Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval, pages 127-134, Toronto, Canada, 2003.
3. D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
4. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Annual Conference on Computational Learning Theory, pages 92-100, Madison, WI, 1998.
5. F. Briggs, F.Z. Xiaoli, and R. Raich. Rank-loss support instance machines for miml instance annotation. In Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 534-542, Beijing, China, 2012.
6. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for SVMs. ACM Transactions on Intelligent Systems and Technology, 2(3):27, 2011.
7. P. Duygulu, K. Barnard, J. F. G. de Freitas, and D. A. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Proceedings of the 7th European Conference on Computer Vision, pages 97-112, London, UK, 2002.
8. T. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1):5228-5235, 2004.
9. M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid. Tagprop: Discriminative metric learning in nearest neighbor models for image autoannotation. In Proceedings of the 12th IEEE International Conference on Computer Vision, pages 309-316, Kyoto, Japan, 2009.
10. V. Jain, E. L.-Miller, and A. McCallum. People-LDA: Anchoring topics to people using face recognition. In Proceedings of the 11th IEEE International Conference on Computer Vision, pages 1-8, Rio de Janeiro, Brazil, October 2007.
11. V. Lavrenko, R. Manmatha, and J. Jeon. A model for learning the semantics of pictures. In Advances in Neural Information Processing Systems 16, pages 553-560. MIT Press, 2003.
12. H. Müller, P. Clough, T. Deselaers, and B. Caputo. ImageCLEF: Experimental Evaluation of Visual Information Retrieval. Springer Publishing Company, Incorporated, Berlin, German, 2010.
13. S. Nowak, K. Nagel, and J. Liebetrau. The CLEF 2011 photo annotation and concept-based retrieval tasks. In Proccedings of the 2011 Conference on Multilingual and Multimodal Information Access Evaluation, Amsterdam, The Netherlands, 2011.
14. D. Putthividhy, H.T. Attias, and S.S. Nagarajan. Topic regression multi-modal latent dirichlet allocation for image annotation. In Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition, pages 3408-3415, San Francisco, CA, 2010.
15. M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In Proccedings of the 20th Conference on Uncertainty in Artificial Intelligence, pages 487-494, Banff, Canada, 2004.
16. T. N. Rubin, A. Chambers, P. Smyth, and M. Steyvers. Statistical topic models for multi-label document classification. Machine Learning, 88(1-2):157- 208, 2012.
17. A. Torralba, K. P. Murphy, and W. T. Freeman. Using the forest to see the trees: Exploiting context for visual object detection and localization. Communications of the ACM, 53(3):107-114, 2010.
18. G. Tsoumakas, I. Katakis, and I. Vlahavas. Mining multi-label data. In Data Mining and Knowledge Discovery Handbook, pages 667-685. Springer, 2010.
19. K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1582-1596, 2010.
20. K-W Wan, A-H Tan, J-H Lim, and L-T Chia. Faceted topic retrieval of news video using joint topic modeling of visual features and speech transcripts. In Proceedings of the 2010 IEEE International Conference Multimedia and Expo, pages 843-848, Singapore, 2010.
21. S.-H. Yang, H. Zha, and B.-G. Hu. Dirichlet-Bernoulli alignment: A generative model for multi-class multi-label multi-instance corpora. In Advances in Neural Information Processing Systems 22, pages 2143-2150. MIT Press, 2009.
22. M-L Zhang and K. Zhang. Multilabel learning by exploiting label dependency. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pages 999- 1008, Washington, DC, 2010.
23. Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In Advances in Neural Information Processing Systems 19, pages 1609-1616. MIT Press, 2007.
24. Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 176(1):2291-2320, 2012.