Dynamic action recognition based on dynemes and Extreme Learning Machine

Pattern Recognition Letters

Volumn 34, Issue 15, 2013, Pages 1890-1898

  Iosifidis, Alexandros ^a   Tefas, Anastasios ^a   Pitas, Ioannis ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Activity recognition; Dynamic classification; Extreme Learning Machine; Fuzzy vector quantization

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; IMAGE RECOGNITION; KNOWLEDGE ACQUISITION; NETWORK LAYERS;

ACTIVITY RECOGNITION; DATA PROJECTION; DYNAMIC ACTIONS; DYNAMIC CLASSIFICATION; EXTREME LEARNING MACHINE; FUZZY VECTOR QUANTIZATION; MULTIPLE LEVELS; SINGLE-HIDDEN LAYER FEEDFORWARD NEURAL NETWORKS;

LEARNING SYSTEMS;

EID: 84885069818     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2012.10.019     Document Type: Article

References (37)

1. Ali, S., Shah, M., 2010. Human action recognition in videos using kinematic features and multiple instance learning. IEEE Trans. Pattern Anal. Machine Intell. 32 (2), 288-303.
2. Ali, S., Shah, M., 2010. Human action recognition in videos using kinematic features and multiple instance learning. IEEE Trans. Pattern Anal. Machine Intell. 32 (2), 288-303.
3. Assfalg, J., Bertini, M., Colombo, C., Del Bimbo, A., Nunziati, W., 2003. Semantic annotation of soccer videos: Automatic highlights identification. Computer Vision and Image Understanding 92 (2-3), 285-305.
4. Barr, P., Noble, J., Biddle, R., 2007. Video game values: Human-computer interaction and games. Interact. Comput. 19 (2), 180-195. (Pubitemid 46162199)
5. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R., 2005. Actions as space-time shapes. Proc. IEEE Conf. Computer Vision 2, 1395-1402. (Pubitemid 44042342)
6. Bradski, G., Kaehler, A., 2008. Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media.
7. Duda, R., Hart, P., Stork, D., 2000. Pattern Classification, second ed. Wiley- Interscience.
8. Frey, B., Dueck, D., 2007. Clustering by passing messages between data points. Science 315 (5814), 972.
9. Giese, M., Poggio, T., 2003. Neural mechanisms for the recognition of biological movements. Nat. Rev. Neurosci. 4 (3), 179-192. (Pubitemid 37280183)
10. Gkalelis, N., Tefas, A., Pitas, I., 2008. Combining fuzzy vector quantization with linear discriminant analysis for continuous human movement recognition. IEEE Trans. Circuits Systems Video Technol. 18 (11), 1511-1521, ISSN 1051-8215.
11. Gkalelis, N., Kim, H., Hilton, A., Nikolaidis, N., Pitas, I., 2009. The i3DPost multi-view and 3D human action/interaction database, 6th Conference on Visual Media. Production, 159-168.
12. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R., 2007. Actions as space-time shapes. IEEE Trans. Pattern Anal. Machine Intell. 29 (12), 2247-2253. (Pubitemid 350124072)
13. Guha, T., Ward, R. Learning sparse representations for human action recognition. IEEE Trans. Pattern Anal. Machine Intell.
14. Haykin, S. Neural Networks and Learning Machines, Upper Saddle River, New Jersey.
15. Hollerer, T., Feiner, S., Hallaway, D., Bell, B., Lanzagorta, M., Brown, D., Julier, S., Baillot, Y., Rosenblum, L., 2001. User interface management techniques for collaborative mobile augmented reality. Comput. Graphics 25 (5), 799-810. (Pubitemid 32833503)
16. Huang, G., Zhu, Q., Siew, C., 2004. Extreme learning machine: A new learning scheme of feedforward neural networks. IEEE Internat. Joint Conf. Neural Networks 2, 985-990. (Pubitemid 40011594)
17. Huang, G., Zhou, H., Ding, X., Zhang, R., 2012. Extreme learning machine for regression and multiclass classification. IEEE Trans. Systems Man Cybernet. Part B: Cybernet. 42 (2), 513-529.
18. Iosifidis, A., Tefas, A., Pitas, I., 2011. Person specific activity recognition using fuzzy learning and discriminant analysis. Eur. Signal Process. Conf. (EUSIPCO), 1974-1978.
19. Iosifidis, A., Tefas, A., Nikolaidis, N., Pitas, I., 2012a. Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis. Computer Vision and Image Understanding 116, 347-360.
20. Iosifidis, A., Tefas, A., Pitas, I., 2012b. View-invariant action recognition based on artificial neural networks. IEEE Trans. Neural Networks Learn. Systems 23 (3), 412-425.
21. Junejo, I., Dexter, E., Laptev, I., Pérez, P., 2011. View-independent action recognition from temporal self-similarities. IEEE Trans. Pattern Anal. Machine Intell. 33 (1), 172-185.
22. Kyperountas, M., Tefas, A., Pitas, I., 2008. Dynamic training using multistage clustering for face recognition. Pattern Recognition 41 (3), 894-905. (Pubitemid 47632665)
23. Lu, J., Plataniotis, K., Venetsanopoulos, A., 2003. Face recognition using LDA-based algorithms. IEEE Trans. Neural Networks 14 (1), 195-200.
24. Lu, J., Plataniotis, K., Venetsanopoulos, A.N., 2005. Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recognition Lett. 26 (2), 181-191.
25. Niebles, J., Wang, H., Fei-Fei, L., 2008. Unsupervised learning of human action categories using spatial-temporal words. Internat. J. Comput. Vision 79 (3), 299-318.
26. Riemenschneider, H., Donoser, M., Bischof, H. Bag of optical flow volumes for image sequence recognition. Br. Machine Vision Conf. (BMVC).
27. Seki, M., Wada, T., Fujiwara, H., Sumi, K., 2003. Background subtraction based on the cooccurrence of image variations. Proc. IEEE Conf. Computer Vision and Pattern Recognition 2, 65-72.
28. Tang, H., Lyu, M., King, I., 2003. Face recognition committee machines: Dynamic vs. static structures. In: Proc. 12th Internat. Conf. Image Anal. Process. pp. 121-126.
29. Thurau, C., Hlavác, V., 2008. Pose primitive based human action recognition in videos or still images. IEEE Conf. on Computer Vision and Pattern Recognition, 1-8.
30. Vezhnevets, V., Sazonov, V., Andreeva, A. A survey on pixel-based skin color detection techniques. Proc. Graphicon 3.
31. Wang, Y., Mori, G., 2009. Human action recognition by semilatent topic models. IEEE Trans. Pattern Anal. Machine Intell. 31 (10), 1762-1774.
32. Wang, Y., Mori, G., 2011. Hidden part models for human action recognition: Probabilistic versus max margin. IEEE Trans. Pattern Anal. Machine Intell. 33 (7), 1310-1323.
33. Weilun, L., Jungong, H., With, P., 2010. Flexible human behavior analysis framework for video surveillance applications. Int. J. Digital Multimedia Broadcast. 2010, 9, ISSN 1687-7578.
34. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y., 2009. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Machine Intell. 31 (2), 210-227.
35. Yeffet, L., Wolf, L., 2009. Local trinary patterns for human action recognition. Internat. Conf. Comput. Vision, 492-497.
36. Yu, S., Tan, D., Tan, T., 2006. Modeling the effect of view angle variation on appearance-based gait recognition. Proc. Asian Conf. Computer Vision 1, 807-816. (Pubitemid 43856536)
37. Zhang, Z., Hu, Y., Chan, S., Chia, L., 2008. Motion context: A new representation for human action recognition. Eur. Conf. Comput. Vision ECCV, 817-829.