Abstract
Recognition of complex dynamic texture is a challenging problem and captures the attention of the computer vision community for several decades. Essentially the dynamic texture recognition is a multi-class classification problem that has become a real challenge for computer vision and machine learning techniques. Existing classifier such as extreme learning machine cannot effectively deal with this problem, due to the reason that the dynamic textures belong to non-Euclidean manifold. In this paper, we propose a new approach to tackle the dynamic texture recognition problem. First, we utilize the affinity propagation clustering technology to design a codebook, and then construct a soft coding feature to represent the whole dynamic texture sequence. This new coding strategy preserves spatial and temporal characteristics of dynamic texture. Finally, by evaluating the proposed approach on the DynTex dataset, we show the effectiveness of the proposed strategy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(2), 513–529 (2012)
Savitha, R., Suresh, S., Kim, H.: A meta-cognitive learning algorithm for an extreme learning machine classifier. Cognitive Computation, 1–11 (2013)
He, B., Xu, D., Nian, R., van Heeswijk, M., Yu, Q., Miche, Y., Lendasse, A.: Fast face recognition via sparse coding and extreme learning machine. Cognitive Computation, 1–14 (2013)
Zong, W., Huang, G.B.: Face recognition based on extreme learning machine. Neurocomputing 74(16), 2541–2551 (2011)
Iosifidis, A., Tefas, A., Pitas, I.: Dynamic action recognition based on dynemes and extreme learning machine. Pattern Recognition Letters 34(15), 1890–1898 (2013)
Lu, B., Wang, G., Yuan, Y., Han, D.: Semantic concept detection for video based on extreme learning machine. Neurocomputing 102, 176–183 (2013)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2(2), 107–122 (2011)
Huang, G.B.: An insight into extreme learning machines: random neurons, random features and kernels. Cognitive Computation, 1–15 (2014)
Chen, J., Zheng, G., Fang, C., Zhang, N., Chen, H., Wu, Z.: Time-series processing of large scale remote sensing data with extreme learning machine. Neurocomputing 128, 199–206 (2014)
Deng, W.Y., Zheng, Q.H., Wang, Z.M.: Cross-person activity recognition using reduced kernel extreme learning machine. Neural Networks 53, 1–7 (2014)
Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. International Journal of Computer Vision 51(2), 91–109 (2003)
Yuan, L., Wen, F., Liu, C., Shum, H.-Y.: Synthesizing dynamic texture with closed-loop linear dynamic system. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 603–616. Springer, Heidelberg (2004)
Liu, H., Xiao, W., Zhao, H., Sun, F.: Learning and understanding system stability using illustrative dynamic texture examples. IEEE Transactions on Education 57(1), 4–11 (2014)
Ravichandran, A., Chaudhry, R., Vidal, R.: Categorizing dynamic textures using a bag of dynamical systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(2), 342–353 (2013)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1794–1801. IEEE (2009)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3360–3367. IEEE (2010)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Ravichandran, A., Chaudhry, R., Vidal, R.: Dynamic texture toolbox (2011), http://www.vision.jhu.edu
Péteri, R., Fazekas, S., Huiskes, M.J.: Dyntex: A comprehensive database of dynamic textures. Pattern Recognition Letters 31(12), 1627–1632 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, L., Liu, H., Sun, F. (2015). Dynamic Texture Video Classification Using Extreme Learning Machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-14066-7_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
Online ISBN: 978-3-319-14066-7
eBook Packages: EngineeringEngineering (R0)