Slow feature analysis for human action recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 34, Issue 3, 2012, Pages 436-450

  Zhang, Zhang ^a   Tao, Dacheng ^b  

^a INSTITUTE OF AUTOMATION   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Human action recognition; slow feature analysis

Indexed keywords

ACTION SEQUENCES; BODY PARTS; CONTROL EXPERIMENTS; CORTICAL NEURONS; FEATURE FUNCTION; FEATURE VECTORS; FIRST ORDER; GENERAL LEARNING; HUMAN-ACTION RECOGNITION; INPUT SIGNAL; LARGE SCALE EXPERIMENTS; LEARNING STRATEGY; LINEAR SUPPORT VECTOR MACHINES; MOTION BOUNDARY; MOTION PATTERN; PROCESSING STEPS; RANDOM SAMPLING; RECEPTIVE FIELDS; RECOGNITION PERFORMANCE; SLOW FEATURE ANALYSIS; SPATIAL RELATIONSHIPS; STATISTICAL DISTRIBUTION; TEMPORAL DERIVATIVES; VISUAL PERCEPTION;

EXPERIMENTS; GESTURE RECOGNITION; IMAGE RETRIEVAL; MOTION ESTIMATION; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; HUMAN; PHYSIOLOGY; SUPPORT VECTOR MACHINE; VISION;

ALGORITHMS; HUMANS; SUPPORT VECTOR MACHINES; VISUAL PERCEPTION;

EID: 84862907634     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2011.157     Document Type: Article

References (49)

1. L. Wiskott and T. Sejnowski, "Slow Feature Analysis: Unsupervised Learning of Invariances," Neural Computation, vol. 14, no. 4, pp. 715-770, Apr. 2002.
2. P. Berkes and L. Wiskott, "Slow Feature Analysis Yields a Rich Repertoire of Complex Cell Properties," J. Vision, vol. 5, no. 6, pp. 579-602, June 2005. (Pubitemid 41519241)
3. M. Franzius, H. Sprekeler, and L. Wiskott, "Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells," PLoS Computational Biology, vol. 3, no. 8, pp. 1605-1622, Aug. 2007. (Pubitemid 47358067)
4. M. Franzius, N. Wilbert, and L. Wiskott, "Invariant Object Recognition with Slow Feature Analysis," Proc. 18th Int'l Conf. Artificial Neural Networks, pp. 961-970, 2008.
5. P. Turaga, R. Chellappa, V.S. Subrahmanian, and O. Udrea, "Machine Recognition of Human Activities: A Survey," IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1473-1488, Sept. 2008.
6. B.A. Olshausen and D.J. Field, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images," Nature, vol. 381, pp. 607-609, June 1996. (Pubitemid 26177476)
7. P.O. Hoyer, "Modeling Receptive Fields with Non-Negative Sparse Coding," Computational Neuroscience: Trends in Research, E.D. Schutter, ed., Elsevier, 2003.
8. C. Chennubhotla and A. Jepson, "Sparse Coding in Practice," Proc. Int'l Workshop Statistical and Computational Theories of Vision, 2001.
9. L. Shang and D. Huang, "Image Denoising Using Non-Negative Sparse Coding Shrinkage Algorithm," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 1017-1022, 2005. (Pubitemid 43897304)
10. B.J. Shastri and M.D. Levine, "Face Recognition Using Localized Features Based on Non-Negative Sparse Coding," Machine Vision and Applications, vol. 18, no. 2, pp. 107-122, Apr. 2007. (Pubitemid 46473851)
11. I. Laptev and T. Lindeberg, "Space-Time Interest Points," Proc. IEEE Int'l Conf. Computer Vision, pp. 432-439, 2003.
12. J.C. Niebles, H. Wang, and L. Fei-Fei, "Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words," Int'l J. Computer Vision, vol. 79, no. 3, pp. 299-318, Sept. 2008.
13. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, "Actions as Space-Time Shapes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2247-2253, Dec. 2007. (Pubitemid 350124072)
14. D. Weinland and E. Boyer, "Action Recognition Using Exemplar-Based Embedding," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 1-7, 2008.
15. A. Bobick and J. Davis, "The Recognition of Human Movement Using Temporal Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 3, pp. 257-267, Mar. 2001. (Pubitemid 32286632)
16. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, "Behavior Recognition via Sparse Spatio-Temporal Features," Proc. IEEE Int'l Workshop Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65-72, 2005. (Pubitemid 46178025)
17. A. Oikonomopoulos, I. Patras, and M. Pantic, "Human Action Recognition with Spatiotemporal Salient Points," IEEE Trans. Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 36, no. 3, pp. 710-719, June 2006. (Pubitemid 43811326)
18. K. Rapantzikos, Y. Avrithis, and S. Kollias, "Dense Saliency-Based Spatiotemporal Feature Points for Action Recognition," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 1454-1461, 2009.
19. W. Lee and H. Chen, "Histogram-Based Interest Point Detectors," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 1590-1596, 2009.
20. Y. Ke, R. Sukthankar, and M. Hebert, "Efficient Visual Event Detection Using Volumetric Features," Proc. IEEE Int'l Conf. Computer Vision, pp. 166-173, 2005. (Pubitemid 44054955)
21. H. Wang, M.M. Ullah, A. Kläser, I. Laptev, and C. Schmid, "Evaluation of Local Spatio-Temporal Features for Action Recognition," Proc. British Machine Vision Conf., 2009.
22. I. Laptev and T. Lindeberg, "Local Descriptors for Spatio-Temporal Recognition," Proc. ECCV Workshop Spatial Coherence for Visual Motion Analysis, 2004.
23. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, "Learning Realistic Human Actions from Movies," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
24. P. Scovanner, S. Ali, and M. Shah, "A 3-Dimensional SIFT Descriptor and Its Application to Action Recognition," Proc. ACM Int'l Conf. Multimedia, pp. 357-360, 2007.
25. A. Klaser, M. Marszalek, and C. Schmid, "A Spatio-Temporal Descriptor Based on 3D-Gradients," Proc. British Machine Vision Conf., 2008.
26. C. Schuldt, I. Laptev, and B. Caputo, "Recognizing Human Actions: A Local SVM Approach," Proc. IEEE Int'l Conf. Pattern Recognition, vol. 3, pp. 32-36, 2004.
27. Z. Zhang, Y. Hu, S. Chan, and L. Chia, "Motion Context: A New Representation for Human Action Recognition," Proc. European Conf. Computer Vision, pp. 817-829, 2008.
28. Y. Wang and G. Mori, "Human Action Recognition by Semi-Latent Topic Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1762-1774, Oct. 2009.
29. J. Liu and M. Shah, "Learning Human Actions via Information Maximization," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2008.
30. J. Liu, S. Ali, and M. Shah, "Recognizing Human Actions Using Multiple Features," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2008.
31. S. Ali, A. Basharat, and M. Shah, "Chaotic Invariants for Human Action Recognition," Proc. IEEE Int'l Conf. Computer Vision, 2007.
32. H. Jhuang, T. Serre, L. Wolf, and T. Poggio, "A Biologically Inspired System for Action Recognition," Proc. IEEE Int'l Conf. Computer Vision, 2007.
33. K. Schindler and L. Gool, "Action Snippets: How Many Frames Does Human Action Recognition Require?" Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2008.
34. M. Bregonzio, S. Gong, and T. Xiang, "Recognising Action as Clouds of Space-Time Interest Points," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2009.
35. S.-F. Wong, T.-K. Kim, and R. Cipolla, "Learning Motion Categories Using Both Semantic and Structural Information," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2007.
36. S. Savarese, A. DelPozo, J. Niebles, and L. Fei-Fei, "Spatial- Temporal Correlatons for Unsupervised Action Classification," Proc. IEEE Workshop Motion and Video Computing, 2008.
37. M.S. Ryoo and J.K. Aggarwal, "Spatio-Temporal Relationship Match: Video Structure Comparison for Recognition of Complex Human Activities," Proc. IEEE Int'l Conf. Computer Vision, 2009.
38. C.M. Bishop, Neural Networks for Pattern Recognition, second ed. Oxford Univ. Press, 1995.
39. T. Kadir and M. Brady, "Scale Saliency: A Novel Approach to Salient Feature and Scale Selection," Proc. Int'l Conf. Visual Information Eng., pp. 25-28, 2003.
40. T. Hofmann, "Probabilistic Latent Semantic Indexing," Proc. Ann. Int'l Conf. Research and Development in Information Retrieval, pp. 50-57, 1999.
41. D.M. Blei, A.Y. Ng, and M.I. Jordan, "Latent Dirichlet Allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, Jan. 2003.
42. M. Giese and T. Poggio, "Neural Mechanisms for the Recognition of Biological Movements and Action," Nature Rev. Neuroscience, vol. 4, pp. 179-192, 2003. (Pubitemid 37280183)
43. CASIA Action Database, http://www.cbsr.ia.ac.cn/english/ Action%20Databases%20EN.asp, 2010.
44. J. Wright, A. Ganesh, A. Yang, and Y. Ma, "Robust Face Recognition via Sparse Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.
45. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Discriminative Learned Dictionaries for Local Image Analysis," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2008.
46. P. Berkes and L. Wiskott, "On the Analysis and Interpretation of Inhomogeneous Quadratic Forms as Receptive Fields," Neural Computation, vol. 18, no. 8, pp. 1868-1895, Aug. 2006. (Pubitemid 44043100)
47. N. Dalal and B. Triggs, "Histogram of Oriented Gradients for Human Detection," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 886-893, 2005. (Pubitemid 43897286)
48. C. Chang and C. Lin, LIBSVM: A Library for Support Vector Machines, Software http://www.csie.ntu.edu.tw/cjlin/libsvm, 2001.
49. M.S. Ryoo and J.K. Aggarwal, An Overview of Contest on Semantic Description of Human Activities (SDHA), Data Set http://cvrc.ece.utexas.edu/ SDHA2010/Human-Interaction.html, 2010.