IEICE TRANSACTIONS on Fundamentals
Dynamic Texture Classification Using Multivariate Hidden Markov Model
Summary :
Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time. Hidden Markov model (HMM) is a statistical model, which has been used to model the dynamic texture. However, the texture is a region property. The traditional HMM models the property of a single pixel along the time, and does not consider the dependence of the spatial adjacent pixels of the dynamic texture. In this paper, the multivariate hidden Markov model (MHMM) is proposed to characterize and classify the dynamic textures. Specifically, the spatial adjacent pixels are modeled with multivariate hidden Markov model, in which the hidden states of those pixels are modeled with the multivariate Markov chain, and the intensity values of those pixels are modeled as the observation variables. Then the model parameters are used to describe the dynamic texture and the classification is based on the maximum likelihood criterion. The experiments on two benchmark datasets demonstrate the effectiveness of the introduced method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.1 pp.302-305
- Publication Date
- 2018/01/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E101.A.302
- Type of Manuscript
- LETTER
- Category
- Image
Authors
Yu-Long QIAO
Harbin Engineering University
Zheng-Yi XING
Harbin Engineering University
Keyword
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Yu-Long QIAO, Zheng-Yi XING, "Dynamic Texture Classification Using Multivariate Hidden Markov Model" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 1, pp. 302-305, January 2018, doi: 10.1587/transfun.E101.A.302.
Abstract: Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time. Hidden Markov model (HMM) is a statistical model, which has been used to model the dynamic texture. However, the texture is a region property. The traditional HMM models the property of a single pixel along the time, and does not consider the dependence of the spatial adjacent pixels of the dynamic texture. In this paper, the multivariate hidden Markov model (MHMM) is proposed to characterize and classify the dynamic textures. Specifically, the spatial adjacent pixels are modeled with multivariate hidden Markov model, in which the hidden states of those pixels are modeled with the multivariate Markov chain, and the intensity values of those pixels are modeled as the observation variables. Then the model parameters are used to describe the dynamic texture and the classification is based on the maximum likelihood criterion. The experiments on two benchmark datasets demonstrate the effectiveness of the introduced method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.302/_p
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@ARTICLE{e101-a_1_302,
author={Yu-Long QIAO, Zheng-Yi XING, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Dynamic Texture Classification Using Multivariate Hidden Markov Model},
year={2018},
volume={E101-A},
number={1},
pages={302-305},
abstract={Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time. Hidden Markov model (HMM) is a statistical model, which has been used to model the dynamic texture. However, the texture is a region property. The traditional HMM models the property of a single pixel along the time, and does not consider the dependence of the spatial adjacent pixels of the dynamic texture. In this paper, the multivariate hidden Markov model (MHMM) is proposed to characterize and classify the dynamic textures. Specifically, the spatial adjacent pixels are modeled with multivariate hidden Markov model, in which the hidden states of those pixels are modeled with the multivariate Markov chain, and the intensity values of those pixels are modeled as the observation variables. Then the model parameters are used to describe the dynamic texture and the classification is based on the maximum likelihood criterion. The experiments on two benchmark datasets demonstrate the effectiveness of the introduced method.},
keywords={},
doi={10.1587/transfun.E101.A.302},
ISSN={1745-1337},
month={January},}
Copy
TY - JOUR
TI - Dynamic Texture Classification Using Multivariate Hidden Markov Model
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 302
EP - 305
AU - Yu-Long QIAO
AU - Zheng-Yi XING
PY - 2018
DO - 10.1587/transfun.E101.A.302
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2018
AB - Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time. Hidden Markov model (HMM) is a statistical model, which has been used to model the dynamic texture. However, the texture is a region property. The traditional HMM models the property of a single pixel along the time, and does not consider the dependence of the spatial adjacent pixels of the dynamic texture. In this paper, the multivariate hidden Markov model (MHMM) is proposed to characterize and classify the dynamic textures. Specifically, the spatial adjacent pixels are modeled with multivariate hidden Markov model, in which the hidden states of those pixels are modeled with the multivariate Markov chain, and the intensity values of those pixels are modeled as the observation variables. Then the model parameters are used to describe the dynamic texture and the classification is based on the maximum likelihood criterion. The experiments on two benchmark datasets demonstrate the effectiveness of the introduced method.
ER -
English
