ACTIVE: Activity concept transitions in video event classification

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 913-920

  Sun, Chen ^a   Nevatia, Ram ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HIDDEN MARKOV MODELS;

EVENT CLASSIFICATION; FEATURE VECTORS; FISHER KERNELS; LOW-LEVEL FEATURES; MULTIMEDIA EVENT DETECTIONS; TRADITIONAL APPROACHES; TRAINING SAMPLE; VISUAL FEATURE;

IMAGE RETRIEVAL;

EID: 84898775956     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.453     Document Type: Conference Paper

References (24)

1. http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.11.org.html.
2. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 2008.
3. A. Farhadi, I. Endres, D. Hoiem, and D. A. Forsyth. Describing objects by their attributes. In CVPR, 2009.
4. A. Gaidon, Z. Harchaoui, and C. Schmid. Actom sequence models for efficient action detection. In CVPR, 2011.
5. H. Izadinia and M. Shah. Recognizing complex events using large margin joint low-level event model. In ECCV, 2012.
6. T. Jaakkola, M. Diekhans, and D. Haussler. A discriminative framework for detecting remote protein homologies. Journal of Computational Biology, 7(1-2), 2000.
7. T. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In NIPS, 1999.
8. A. Kl̈aser, M. Marszalek, and C. Schmid. A spatio-temporal descriptor based on 3d-gradients. In BMVC, 2008.
9. I. Laptev. On space-time interest points. IJCV, 64(2-3):107-123, 2005.
10. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, 2008.
11. L.-J. Li, H. Su, E. P. Xing, and F.-F. Li. Object bank: A highlevel image representation for scene classification & semantic feature sparsification. In NIPS, 2010.
12. J. Liu, B. Kuipers, and S. Savarese. Recognizing human actions by attributes. In CVPR, 2011.
13. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
14. P. Natarajan, S. Vitaladevuni, U. Park, S. Wu, V. Manohar, X. Zhuang, S. Tsakalidis, R. Prasad, and P. Natarajan. Multimodel feature fusion for robust event detection in web videos. In CVPR, 2012.
15. F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. In CVPR, 2007.
16. L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. In Proceedings of the IEEE, 1989.
17. C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In ICPR, 2004.
18. K. Soomro, A. R. Zamir, and M. Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. CRCVTR-12-01.
19. C. Sun and R. Nevatia. Large-scale web video event classification by use of fisher vectors. In WACV, 2013.
20. A. Tamrakar, S. Ali, Q. Yu, J. Liu, O. Javed, A. Divakaran, H. Cheng, and H. S. Sawhney. Evaluation of low-level features and their combinations for complex event detection in open source videos. In CVPR, 2012.
21. K. Tang, F.-F. Li, and D. Koller. Learning latent temporal structure for complex event detection. In CVPR, 2012.
22. L. Torresani, M. Szummer, and A. W. Fitzgibbon. Efficient object category recognition using classemes. In ECCV, 2010.
23. H.Wang, A. Kl̈aser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In CVPR, 2011.
24. H. Wang, M. M. Ullah, A. Kl̈aser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. In BMVC, 2009.