University of Central Florida at TRECVID 2006 high-level feature extraction and video search

2006 TREC Video Retrieval Evaluation Notebook Papers

Volumn , Issue , 2006, Pages

  Liu, Jingen ^a   Zhai, Yun ^a   Basharat, Arslan ^a   Orhan, Bilal ^a   Khan, Saad M ^a   Noor, Humera ^a   Berkowitz, Phillip ^a   Shah, Mubarak ^a  

^a University of Central Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; EXPERIMENTS; FEATURE EXTRACTION; USER INTERFACES;

CLASSIFICATION RESULTS; COMBINED CLASSIFIERS; HIGH-LEVEL FEATURE EXTRACTIONS; HIGH-LEVEL FEATURES; INDIVIDUAL FEATURES; NEWS VIDEO RETRIEVALS; RELEVANCE FEEDBACK; UNIVERSITY OF CENTRAL FLORIDA;

IMAGE RETRIEVAL;

EID: 84905178815     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. B. E. Boster, I. Guyon and V. Vapnik. A Training Algorithm for Optimal Margin Classifiers. In COLT, pp. 144-152, 1992.
2. J. Liu, Y. Zhai and M. Shah, PAGASUS: An Information Mining System for TV News Videos, SPIE Defense and Security Symposium, April 17-21, Orlando, 2005.
3. Y. Zhai, J. Liu and M. Shah, Automatic Query Expansion in News Video Retrieval, International Conference on Multimedia and Expo (ICME), July 7-12, 2006. Toronto, Canada.
4. Y. Zhai, J. Liu, X. Cao, A. Basharat, A. Hakeem, S. Ali, M. Shah, C. Grana and R. Cucchiarra, Video Understanding and Content-Based Retrieval, TREC Video Retrieval Evaluation Workshop (TRECVID 2005), Gaithersbury, Maryland, November 14-15, 2005.
5. J. Huang, S. Kumar, M. Mitra, W. Zhu and R. Zabih, Image Indexing Using Color Correlogram, Proceeding of Computer Vision and Pattern Recognition, pp. 762-768, 1997.
6. A. Amir, J. Argillander, et al., IBM Research TRECVID-2005 video Retrieval System, TREC Video Retrieval Evaluation Workshop (TRECVID 2005), Gaithersbury, Maryland, November 14-15, 2005.
7. S. Chang, W. Hsu, et al. Columbia University TRECVID-2005 Video Retrieval and High Level Feature Extraction, TREC Video Retrieval Evaluation Workshop (TRECVID 2005), Gaithersbury, Maryland, November 14-15, 2005.
8. D. Haussler, Decision Theoretic Genelization of the PAC model for neural net and other learning applications. Information and Computation, 100: 78-150, 1992.
9. S. Brandt, J. Laaksonen and E. Oja, Statistical Shape Features for Centent-Based Image Retrieval, Journal of Mathematical Imaging and Vision, pp187-198, 2002.
10. R. Yong and T. Huang, A Novel Relevance Feedback Technique in Image Retrieval, ACM Multimedia, 1999.
11. Scott, D. W., Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley-Interscience, 1992.
12. C. Hsu, C. Chang, and C. Lin. A Practical Guide to Support Vector Classification. http://www. csie. ntu. edu. tw/cjlin/papers/guide/guide. pdf.
13. Thomas Hofmann, "Unsupervised Learning by Probabilistic Latent Semantic Analysis, " Machine Learning, Vol. 42, pp. 177-196, 2001.
14. D. Comaniciu and P. Meer. Mean shift: A Robust Approach Toward Feature Space Analysis, PAMI, 24, pp603-619, 2002.
15. Y. Rubner, C. Tomasi and L. Guibas, A Metric for Distributions with Applications to Image Databases, ICCV, 1998.