Abstract
Classification of dynamic textures is an important and meaningful research in texture analysis. To accurately describe and classify dynamic textures, this paper proposes Continuous Hidden Markov Model (CHMM) based method. Specifically, the implicit state variable in CHMM represents the motion information of the dynamic texture with time, and the mixed Gaussian function is used to fit the observed gray value information of the texture at the spatial position. Then, a new dynamic texture sequence is assigned to the most similar category, by calculating the maximum likelihood probability generated by the trained dynamic textures CHMM models. The experimental results on the benchmark DynTex database demonstrate that CHMM is superior to the LDS based method and DHMM based method, for obtaining higher correct classification rate.
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Acknowledgments
This work is partially supported by National Natural Science Foundation of China 61371175 and Fundamental Research Funds for the Central Universities HEUCFQ20150812.
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Qiao, Y., Li, N., Wang, Y., Xi, W. (2018). CHMM-Based Classification of Dynamic Textures. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_5
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DOI: https://doi.org/10.1007/978-981-10-7605-3_5
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