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2018 | OriginalPaper | Chapter

CHMM-Based Classification of Dynamic Textures

Authors : Yulong Qiao, Na Li, Yufei Wang, Wei Xi

Published in: Advances in Computer Science and Ubiquitous Computing

Publisher: Springer Singapore

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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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Metadata
Title
CHMM-Based Classification of Dynamic Textures
Authors
Yulong Qiao
Na Li
Yufei Wang
Wei Xi
Copyright Year
2018
Publisher
Springer Singapore
Book
Advances in Computer Science and Ubiquitous Computing

Print ISBN: 978-981-10-7604-6

Electronic ISBN: 978-981-10-7605-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7605-3_5