The Many Shades of Negativity

IEEE Transactions on Multimedia

Volumn 19, Issue 7, 2017, Pages 1558-1568

  Ma, Zhigang ^a   Chang, Xiaojun ^a   Yang, Yi ^b   Sebe, Nicu ^c   Hauptmann, Alexander G ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c UNIVERSITY OF TRENTO   (Italy)

Author keywords

Attribute representation; attribute selection; complex event detection; selective fine grained labeling

Indexed keywords

COMPLEX NETWORKS; DEEP NEURAL NETWORKS; EDUCATION; INDEXING (OF INFORMATION); NEURAL NETWORKS;

ATTRIBUTE REPRESENTATION; ATTRIBUTE SELECTION; CLASSIFIER LEARNING; CLASSIFIER TRAINING; COMPLEX EVENT DETECTION; CONVOLUTIONAL NEURAL NETWORK; FINE GRAINED; VIDEO INDEXING AND RETRIEVAL;

IMAGE RETRIEVAL;

EID: 85021343080     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2017.2659221     Document Type: Article

References (34)

1. A. G. Hauptmann, R. Yan, W. Lin, M. G. Christel, and H. D. Wactlar, "Can high-level concepts fill the semantic gap in video retrieval? A case study with broadcast news, " IEEE Trans. Multimedia, vol. 9, no. 5, pp. 958-966, Aug. 2007.
2. Y. Jiang, J. Yang, C. Ngo, and A. G. Hauptmann, "Representations of keypoint-based semantic concept detection: A comprehensive study, " IEEE Trans. Multimedia, vol. 12, no. 1, pp. 42-53, Jan. 2010.
3. A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz, "Robust real-time unusual event detection using multiple fixed-location monitors, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 3, pp. 555-560, Mar. 2008.
4. D. Xu and S. Chang, "Video event recognition using kernel methods with multilevel temporal alignment, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 11, pp. 1985-1997, Nov. 2008.
5. E. Ricci, G. Zen, N. Sebe, and S. Messelodi, "A prototype learning framework using EMD: application to complex scenes analysis, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 3, pp. 513-526, Mar. 2013.
6. Y. Yang et al., "Multi-feature fusion via hierarchical regression for multimedia analysis, " IEEE Trans. Multimedia, vol. 15, no. 3, pp. 572-581, Apr. 2013.
7. Z. Ma, Y. Yang, Z. Xu, N. Sebe, and A. G. Hauptmann, "We are not equally negative: fine-grained labeling for multimedia event detection, " in Proc. 21st ACM Int. Conf. Multimedia, 2013, pp. 293-302 .
8. A. Vahdat, K. J. Cannons, G. Mori, S. Oh, and I. Kim, "Compositional models for video event detection: A multiple kernel learning latent variable approach, " in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 1185-1192.
9. Y. Cheng, Q. Fan, S. Pankanti, and A. N. Choudhary, "Temporal sequence modeling for video event detection, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 2235-2242.
10. Z. Ma, Y. Yang, N. Sebe, and A. G. Hauptmann, "Knowledge adaptation with partiallyshared features for event detectionusing few exemplars, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 9, pp. 1789-1802, Sep. 2014.
11. X. Chang, Y. Yang, G. Long, C. Zhang, and A. G. Hauptmann, "Dynamic concept composition for zero-example event detection, " in Proc. 30th AAAI Conf. Artif. Intell., 2016, pp. 3464-3470 .
12. S. Bhattacharya, M. M. Kalayeh, R. Sukthankar, and M. Shah, "Recognition of complex events: Exploiting temporal dynamics between underlying concepts, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 2243-2250.
13. K. Lai, F. X. Yu, M. Chen, and S. Chang, "Video event detection by inferring temporal instance labels, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 2251-2258.
14. G. Ye, Y. Li, H. Xu, D. Liu, and S. Chang, "Eventnet: A large scale structured concept library for complex event detection in video" in Proc. ACM MM, 2015, pp. 471-480.
15. A. Habibian, T. Mensink, and C. G. M. Snoek, "Videostory: A new multimedia embedding for few-example recognition and translation of events, " in Proc. 23rd ACM Int. Conf. Multimedia, 2014, pp. 471-480.
16. Z. Xu, Y. Yang, and A. G. Hauptmann, "A discriminative CNN video representation for event detection, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2015, pp. 1798-1807.
17. X. Chang, Y. Yu, Y. Yang, and E. P. Xing, "They are not equally reliable: Semantic event search using differentiated concept classifiers, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2016, pp. 1884-1893.
18. X. Chang, Y.-L. Yu, Y. Yang, and E. P. Xing, "Semantic pooling for complex event analysis in untrimmed videos, " IEEE Trans. Pattern Anal. Mach. Intell., to be published.
19. M. Merler, B. Huang, L. Xie, G. Hua, and A. Natsev, "Semantic model vectors for complex video event recognition, " IEEE Trans. Multimedia, vol. 14, no. 1, pp. 88-101, Feb. 2012.
20. A. Habibian, K. E. A. van de Sande, and C. G. M. Snoek, "Recommendations for video event recognition using concept vocabularies, " in Proc. 3rd ACM Conf. Int. Conf. Multimedia Retrieval, 2013, pp. 89-96 .
21. K. Soomro, A. R. Zamir, and M. Shah, "UCF101: A dataset of 101 human actions classes from videos in the wild, " CoRR, 2012. [Online]. Available: http://arxiv. org/abs/1212. 0402
22. B. Schölkopf, A. J. Smola, andK. Müller, "Nonlinear component analysis as a kernel eigenvalue problem" Neural Comput., vol. 10, no. 5, pp. 1299-1319, 1998.
23. Y. Yang, H. T. Shen, Z. Ma, Z. Huang, and X. Zhou, "l2, 1-norm regularized discriminative feature selection for unsupervised learning" in Proc. 22nd Int. Joint Conf. Artif. Intell., 2011, pp. 1589-1594.
24. NIST, "The Trecvid MED 2014 evaluation plan, " 2014. [Online]. Available: http://nist. gov/itl/iad/mig/med14. cfm
25. L. Jiang et al., "Cmu informedia@ trecvid 2014: Semantic indexing, " 2014.
26. A. Karpathy et al., "Large-scale video classification with convolutional neural networks, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 1725-1732.
27. L. Jiang et al., "Self-paced learning with diversity" in Proc. Adv. Neural Inf. Process. Syst., 2014, pp. 2078-2086.
28. B. Thomee et al., "The new data and new challenges in multimedia research, " CoRR, 2015. [Online]. Available: http://arxiv. org/abs/1503. 01817
29. H. Wang and C. Schmid, "Action recognition with improved trajectories, " in Proc. Int. Conf. Comput. Vis., 2013, pp. 3551-3558.
30. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition, " CoRR, 2014. [Online]. Available: http://arxiv. org/abs/1409. 1556
31. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks" in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097-1105.
32. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, "Return of the devil in the details: Delving deep into convolutional nets" in Proc. Brit. Mach. Vis. Conf., 2014.
33. G. Ye, D. Liu, I. Jhuo, and S. Chang, "Robust late fusion with rank minimization, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2012, pp. 3021-3028.
34. S.-I. Yu et al., "Informedia@TRECVID 2014 MED and MER. "