Transductive inference with hierarchical clustering for video annotation

Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Volumn , Issue , 2007, Pages 643-646

  Qi, Guo Jun ^a   Hua, Xian Sheng ^b   Song, Yan ^a   Tang, Jinhui ^a   Zhang, Hong Jiang ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; FORECASTING; ITERATIVE METHODS; SEMANTICS;

CLUSTERING RESULTS; GENERALIZATION ABILITY; HIER-ARCHICAL CLUSTERING; INDUCTIVE INFERENCE; TRANSDUCTIVE INFERENCE; TUNING ALGORITHM; UNLABELED SAMPLES; VIDEO ANNOTATIONS;

CLASSIFICATION (OF INFORMATION);

EID: 46449110097     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icme.2007.4284732     Document Type: Conference Paper

References (7)

1. G.-J. Qi, Y. Song, and X.-S. Hua, etc. Video Annotation by Active Learning and Cluster Tuning. SLAM 2006, in association with CVPR 2006.
2. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998
3. P. Derbeko, et al Error Bounds for Transductive Learning via Compression and Clustering. Advances in Neural Information Processing Systems (NIPS) 16, pp 1085-1092, 2003.
4. A. Ng and M. Jordan. On Spectral Clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems 14: Proceedings of the 2001.
5. R. El-Yaniv and L. Gerzon. Effective Transductive Learning via PAC-Bayesian Model Selection. Technical Report CS-2004-05, Technion - Israel Institute of Technology, 2004.
6. Guidelines for the TRECVID 2003 Evaluation http://wwwnlpir.nist.gov/ projects/tv2003/tv2003.html
7. Y.Song, X.-S. Hua and L.-R. Dai, etc. Semi-Automatic Video Annotation Based on Active Learning with Multiple Complementary Predictors. Workshop on Multimedia Information Retrieval 2005 (MIR 2005 - ACM MM 2005 Workshop).