• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Chaotic Features for Dynamic Textures Recognition with Group Sparsity Representation

Vol. 9, No. 11, November 29, 2015
10.3837/tiis.2015.11.017, Download Paper (Free):

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.


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Cite this article

[IEEE Style]
X. Luo, S. Fu and Y. Wang, "Chaotic Features for Dynamic Textures Recognition with Group Sparsity Representation," KSII Transactions on Internet and Information Systems, vol. 9, no. 11, pp. 4556-4572, 2015. DOI: 10.3837/tiis.2015.11.017.

[ACM Style]
Xinbin Luo, Shan Fu, and Yong Wang. 2015. Chaotic Features for Dynamic Textures Recognition with Group Sparsity Representation. KSII Transactions on Internet and Information Systems, 9, 11, (2015), 4556-4572. DOI: 10.3837/tiis.2015.11.017.

[BibTeX Style]
@article{tiis:20947, title="Chaotic Features for Dynamic Textures Recognition with Group Sparsity Representation", author="Xinbin Luo and Shan Fu and Yong Wang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2015.11.017}, volume={9}, number={11}, year="2015", month={November}, pages={4556-4572}}