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http://dx.doi.org/10.3837/tiis.2015.11.017

Chaotic Features for Dynamic Textures Recognition with Group Sparsity Representation  

Luo, Xinbin (School of Aeronautics and Astronautics, Shanghai Jiao Tong University)
Fu, Shan (School of Aeronautics and Astronautics, Shanghai Jiao Tong University)
Wang, Yong (School of Aeronautics and Astronautics, Shanghai Jiao Tong University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.9, no.11, 2015 , pp. 4556-4572 More about this Journal
Abstract
Dynamic texture (DT) recognition is a challenging problem in numerous applications. In this study, we propose a new algorithm for DT recognition based on group sparsity structure in conjunction with chaotic feature vector. Bag-of-words model is used to represent each video as a histogram of the chaotic feature vector, which is proposed to capture self-similarity property of the pixel intensity series. The recognition problem is then cast to a group sparsity model, which can be efficiently optimized through alternating direction method of multiplier algorithm. Experimental results show that the proposed method exhibited the best performance among several well-known DT modeling techniques.
Keywords
Dynamic textures recognition; chaotic feature vector; group sparsity;
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1 G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto, “Dynamic texture,” International Journal of Computer Vision, 51(2), pp. 91-109, 2003. Article (CrossRef Link)   DOI
2 Kantz H and Schreiber T 1997 Nonlinear Time Series Analysis (Cambridge: Cambridge University Press)
3 S. Ali, A. Basharat, and M. Shah, "Chaotic invariants for human action recognition," IEEE International Conference on Computer Vision, 2007. Article (CrossRef Link)
4 N. Shroff, P. Turaga, and R. Chellappa, "Moving Vistas: Exploiting Motion for Describing Scenes," in Proc. of IEEE conference on Computer Vision and Pattern Recognition (CVPR), June 2010. Article (CrossRef Link)
5 S. Wu, B. Moore, and M. Shah, "Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. Article (CrossRef Link)
6 Jalali A, Ravikumar P D, Sanghavi S, et al., “A Dirty Model for Multi-task Learning[C],” NIPS, 3: 7, 2010. Article (CrossRef Link)
7 A.P.Pentland, “Fractal Based Description of Natural Scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 6(6), pp. 661-674, 1984. Article (CrossRef Link)   DOI
8 D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” Journal Optical Society America, vol. A4, pp. 2379-2394, 1987. Article (CrossRef Link)   DOI
9 Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y., “Robust face recognition via sparse representation,” PAMI 31 pp. 210–227, 2009. Article (CrossRef Link)   DOI
10 A. Ravichandran, R. Chaudhry, and R. Vidal, "View-Invariant Dynamic Texture Recognition using a Bag of Dynamical Systems," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2009. Article (CrossRef Link)
11 A. B. Chan and N. Vasconcelos, "Mixtures of dynamic textures," in Proc. of IEEE International Conference on Computer Vision, vol. 1, pp. 641-7, 2005. Article (CrossRef Link)
12 A. B. Chan and N. Vasconcelos, "Classifying video with kernel dynamic textures," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2007, Minneapolis. Article (CrossRef Link)
13 Chaudhuri BB, "Sakar N. Texture segmentation using fractal dimension," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 17, pp. 72-77, 1995. Article (CrossRef Link)
14 A. B. Chan and N.Vasconcelos, "Probabilistic kernels for the classification of auto-regressive visual processes," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005. Article (CrossRef Link)
15 A. Ravichandran, R. Chaudhry, and R. Vidal, "Categorizing Dynamic Textures using a Bag of Dynamical Systems," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2012. Article (CrossRef Link)
16 F. Taken, "Detecting Strange Attractors in Turbulence," Lecture Notes in Mathematics, ed D. A.Rand& L. S. Young, 1981. Article (CrossRef Link)
17 Yuan, X., & Yan, S., "Visual classification with multi-task joint sparse representation" in Proc. of IEEE conference on computer vision and pattern recognition, pp. 3493-3500, 2010. Article (CrossRef Link)
18 Quattoni, A., Carreras, X., Collins, M.,& Darrell, T, "An efficient projection for l 1, infinity regularization," in Proc. of International conference on machine learning, pp. 857-864, 2009. Article (CrossRef Link)
19 A. M. Fraser et. al., “Independent Coordinates for Strange Attractors from Mutual Information,” Phys. Rev., 1986. Article (CrossRef Link)
20 M. B. Kennel et. al, “Determining Embedding Dimension for Phase Space Reconstruction using A Geometrical Construction,” Phys. Rev.A, 45, 1992. Article (CrossRef Link)   DOI
21 S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006. Article (CrossRef Link)
22 Chen, X., Pan, W., Kwok, J., & Carbonell, J., "Accelerated gradient method for multi-task sparse learning problem," in Proc. of IEEE international conference on data mining, pp. 746-751, 2009. Article (CrossRef Link)
23 Fei-Fei, L. and Perona, P, "A Bayesian hierarchical model for learning natural scene categories," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 524 - 531, vol. 2, June 2005. Article (CrossRef Link)
24 Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein. J., “Distributed optimization and statistical learning via the alternating direction method of multipliers” Found. Trends Mach. Learn., 3(1):1–122, 2010. Article (CrossRef Link)   DOI
25 S. Fazekas T. Amiaz, D. Chetverikov, and N. Kiryati, “Dynamic texture detection based on motion analysis,” Int. J. Comput. Vis., vol. 82, no. 1, pp. 48–63, 2009. Article (CrossRef Link)   DOI
26 K. G. Derpanis and R. P.Wildes. "Dynamic texture recognition based on distributions of spacetime oriented." CVPR, 2010. Article (CrossRef Link)
27 B. Ghanem and N. Ahuja. "Maximum margin distance learning for dynamic texture recognition," ECCV, pp. 223-236, 2010. Article (CrossRef Link)
28 A. Fournier and W. Reeves, "A simple model of ocean waves," in Proc. of ACM SIGGRAPH, pp. 75-84, 1986. Article (CrossRef Link)
29 Saisan, P., Doretto, G., Wu, Y. N., and Soatto, S., "Dynamic texture recognition," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 58-63, 2001. Article (CrossRef Link)
30 R.Peteri, and D.Chetverikov, "Dynamic Texture Recognition Using Normal Flow and Texture Regularity," in Proc. of Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2005), Estoril, Portugal, pp.223-230, 2005. Article (CrossRef Link)
31 Wang Y, Hu S, “Chaotic features for dynamic textures recognition[J],” Soft Computing, pp. 1-13, 2015. Article (CrossRef Link)
32 D.Chetverikov, and R.Péteri, "A Brief Survey of Dynamic Texture Description and Recognition," in Proc. of 4th Int. Conf. on Computer Recognition Systems, Poland, pp.17-26, 2005.Article (CrossRef Link)
33 P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, "Behavior recognition via sparse spatio-temporal features," VS-PETS, 2005. Article (CrossRef Link)
34 A. Oliva and A. Torralba, “Modeling the shape of the scene: A holistic representation of the spatial envelope,” International Journal of Computer Vision, 2001. Article (CrossRef Link)