IEICE TRANSACTIONS on Information
Adaptive Decomposition of Dynamic Scene into Object-Based Distribution Components Based on Mixture Model Framework
Summary :
This paper newly proposes a method to automatically decompose real scene images into multiple object-oriented component regions. First, histogram patterns of a specific image feature, such as intensity or hue value, are estimated from image sequence and stored up. Next, Gaussian distribution parameters which correspond to object components involved in the scene are estimated by applying the EM algorithm to the accumulated histogram. The number of the components is simultaneously estimated by evaluating the minimum value of Bayesian Information Criterion (BIC). This method can be applied to a variety of computer vision issues, for example, the color image segmentation and the recognition of scene situation transition. Experimental results applied for indoor and outdoor scenes showed the effectiveness of the proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.4 pp.758-766
- Publication Date
- 2005/04/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.4.758
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Mutsumi WATANABE, "Adaptive Decomposition of Dynamic Scene into Object-Based Distribution Components Based on Mixture Model Framework" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 4, pp. 758-766, April 2005, doi: 10.1093/ietisy/e88-d.4.758.
Abstract: This paper newly proposes a method to automatically decompose real scene images into multiple object-oriented component regions. First, histogram patterns of a specific image feature, such as intensity or hue value, are estimated from image sequence and stored up. Next, Gaussian distribution parameters which correspond to object components involved in the scene are estimated by applying the EM algorithm to the accumulated histogram. The number of the components is simultaneously estimated by evaluating the minimum value of Bayesian Information Criterion (BIC). This method can be applied to a variety of computer vision issues, for example, the color image segmentation and the recognition of scene situation transition. Experimental results applied for indoor and outdoor scenes showed the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.4.758/_p
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@ARTICLE{e88-d_4_758,
author={Mutsumi WATANABE, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Decomposition of Dynamic Scene into Object-Based Distribution Components Based on Mixture Model Framework},
year={2005},
volume={E88-D},
number={4},
pages={758-766},
abstract={This paper newly proposes a method to automatically decompose real scene images into multiple object-oriented component regions. First, histogram patterns of a specific image feature, such as intensity or hue value, are estimated from image sequence and stored up. Next, Gaussian distribution parameters which correspond to object components involved in the scene are estimated by applying the EM algorithm to the accumulated histogram. The number of the components is simultaneously estimated by evaluating the minimum value of Bayesian Information Criterion (BIC). This method can be applied to a variety of computer vision issues, for example, the color image segmentation and the recognition of scene situation transition. Experimental results applied for indoor and outdoor scenes showed the effectiveness of the proposed method.},
keywords={},
doi={10.1093/ietisy/e88-d.4.758},
ISSN={},
month={April},}
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TY - JOUR
TI - Adaptive Decomposition of Dynamic Scene into Object-Based Distribution Components Based on Mixture Model Framework
T2 - IEICE TRANSACTIONS on Information
SP - 758
EP - 766
AU - Mutsumi WATANABE
PY - 2005
DO - 10.1093/ietisy/e88-d.4.758
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2005
AB - This paper newly proposes a method to automatically decompose real scene images into multiple object-oriented component regions. First, histogram patterns of a specific image feature, such as intensity or hue value, are estimated from image sequence and stored up. Next, Gaussian distribution parameters which correspond to object components involved in the scene are estimated by applying the EM algorithm to the accumulated histogram. The number of the components is simultaneously estimated by evaluating the minimum value of Bayesian Information Criterion (BIC). This method can be applied to a variety of computer vision issues, for example, the color image segmentation and the recognition of scene situation transition. Experimental results applied for indoor and outdoor scenes showed the effectiveness of the proposed method.
ER -
English
