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http://dx.doi.org/10.3837/tiis.2014.11.027

Exploiting Chaotic Feature Vector for Dynamic Textures Recognition  

Wang, Yong (school of Aeronautics and Astronautics, Shanghai Jiao Tong University)
Hu, Shiqiang (School of Aeronautics and Astronautics, Shanghai Jiao Tong University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.8, no.11, 2014 , pp. 4137-4152 More about this Journal
Abstract
This paper investigates the description ability of chaotic feature vector to dynamic textures. First a chaotic feature and other features are calculated from each pixel intensity series. Then these features are combined to a chaotic feature vector. Therefore a video is modeled as a feature vector matrix. Next by the aid of bag of words framework, we explore the representation ability of the proposed chaotic feature vector. Finally we investigate recognition rate between different combinations of chaotic features. Experimental results show the merit of chaotic feature vector for pixel intensity series representation.
Keywords
Chaotic feature vector; pixel intensity series; bag of words; dynamic textures recognition;
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1 C. Schuldt and I. Laptev, "Recognizing human actions: A local SVM approach," in Proc. of the International Conference on Pattern Recognition, vol. 3, pp. 32-36, 2004.
2 Scovanner P, Ali S, Shah M, "A 3-dimensional sift descriptor and its application to action recognition," in Proc. of the 15th international conference on Multimedia, ACM, pp. 357-360, 2007.
3 Li, T., Mei, T., Kweon, I. S., and Hua, X. S, "Contextual bag-of-words for visual categorization," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 4, pp. 381-392, 2011.   DOI   ScienceOn
4 Li, T., Yan, S., Mei, T., Hua, X. S., and Kweon, I. S., "Image decomposition with multilabel context: Algorithms and applications," IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2301-2314, 2011.   DOI   ScienceOn
5 Kantz H and Schreiber T, "Nonlinear Time Series Analysis," (Cambridge: Cambridge University Press), 1997.
6 M. Perc, "The Dynamics of Human Gait," European Journal of Physics, vol. 26, pp. 525-534, 2005.   DOI   ScienceOn
7 Chaudhuri BB, Sakar N, "Texture segmentation using fractal dimension," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, pp:72-77, 1995.   DOI   ScienceOn
8 A.P.Pentland, "Fractal Based Description of Natural Scenes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, no. 6, pp. 661-674, 1984.
9 D. J. Field, "Relations between the statistics of natural images and the response properties of cortical cells," Journal Optical Society America, 1987, vol. A4, pp. 2379-2394.
10 B. Ghanem and N. Ahuja, "Maximum margin distance learning for dynamic texture recognition," in Proc. of European Conference on Computer Vision, pp. 223-236, 2010.
11 F. Taken, "Detecting Strange Attractors in Turbulence," Lecture Notes in Mathematics, ed D. A.Rand & L. S. Young, 1981.
12 A. M. Fraser and H. L. Swinney, "Independent Coordinates for Strange Attractors from Mutual Information," Physical Review A, vol. 33, no. 2, pp. 1134-1140, February, 1986.   DOI   ScienceOn
13 M. B. Kennel, R. Brown and H. D. I. Abarbanel, "Determining Embedding Dimension for Phase Space Reconstruction using A Geometrical Construction," Physical Review A, vol. 45, no. 6, pp. 3403-3411, June, 1992.   DOI   ScienceOn
14 Saisan, P., Doretto, G., Wu, Y. N., and Soatto, S., "Dynamic texture recognition," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 58-63, 2001.
15 S. Roweis and L. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, 2000.   DOI   ScienceOn
16 J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, 2000.   DOI   ScienceOn
17 Yasmin Mussarat, Sharif Muhammad, Mohsin Sajjad and Irum Isma, "Content Based Image Retrieval Using Combined Features of Shape, Color and Relevance Feedback," KSII Transactions on Internet and Information Systems, vol. 7, no. 12, pp. 3149-3165. December, 2013.   DOI   ScienceOn
18 Huy Hoang Nguyen, GueeSang Lee, SooHyung Kim and Hyung Jeong Yang, "An Effective Orientation-based Method and Parameter Space Discretization for Defined Object Segmentation," KSII Transactions on Internet and Information Systems, vol. 7, no. 12, pp. 3180-3199, December, 2013.   DOI   ScienceOn
19 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, pp. 1911-1918, June 13-18, 2010.
20 Rongrong Ji, Yue Gao, Richang Hong, Qiong Liu, Dacheng Tao, and Xuelong Li, "Spectral-Spatial Constraint Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 3, pp. 1811-1824, march 2013.
21 Derpanis K G, Wildes R P, "Spacetime texture representation and recognition based on a spatiotemporal orientation analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 6, pp. 1193-1205, 2012.   DOI   ScienceOn
22 G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto, "Dynamic texture," International Journal of Computer Vision, vol. 51, no. 2, pp. 91-109, 2003.   DOI
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, vol. 2, pp. 524-531 June, 2005.
24 Rongrong Ji, Hongxun Yao, Qi Tian, Pengfei Xu, Xiaoshuai Sun, and Xianming Liu, "Context-Aware Semi-Local Feature Detector," ACM Transactions on Intelligent System and Technology , vol. 3, no. 3, pp. 44-71, 2012.
25 S. Ali, A. Basharat, and M. Shah, "Chaotic invariants for human action recognition," in Proc. of IEEE International Conference on Computer Vision, pp. 1-8, October 14-20, 2007.
26 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, vol. 2, pp. 2169-2178, 2006.
27 S.Fazekas, and D.Chetverikov, "Normal Versus Complete Flow in Dynamic Texture Recognition: A Comparative Study," in Proc. of 4th International Workshop on Texture Analysis and Synthesis, pp.37-42, 2005.
28 D.Chetverikov, and R.Peteri, "A Brief Survey of Dynamic Texture Description and Recognition," in Proc. of 4th Int. Conference on Computer Recognition Systems, Poland, pp.17-26, 2005.
29 A. Ravichandran, R. Chaudhry, and R. Vidal, "Categorizing Dynamic Textures using a Bag of Dynamical Systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 2, pp. 342-353, 2013.   DOI   ScienceOn
30 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.
31 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, June, 2005.
32 A. B. Chan and N. Vasconcelos, "Classifying video with kernel dynamic textures," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, June, 2007.
33 P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, "Behavior recognition via sparse spatio-temporal features," Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65-72, 2005.
34 S. Wu, B. Moore, and M. Shah, "Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2054-2060, June 13-18, 2010.