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A survey of graph theoretical approaches to image segmentation

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WOS被引频次:114
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成果类型:
期刊论文
作者:
Peng, Bo;Zhang, Lei;Zhang, David
通讯作者:
Zhang, L
作者机构:
[Peng, Bo; Zhang, Lei; Zhang, David] Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong
[Peng, Bo] Department of Software Engineering, Southwest Jiaotong University, Chengdu, China
通讯机构:
[Zhang, Lei] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China.
语种:
英文
关键词:
Image segmentation;Graph theoretical methods;Minimal spanning tree;Graph cut
期刊:
Pattern Recognition
ISSN:
0031-3203
年:
2013
卷:
46
期:
3
页码:
1020-1038
文献类别:
WOS:Article;EI:Journal article (JA)
所属学科:
ESI学科类别:工程学;WOS学科类别:Computer Science, Artificial Intelligence;Engineering, Electrical & Electronic
入藏号:
WOS:000313385700035;EI:20124915745331
基金类别:
National Natural Science Foundation of China [61202190, 61175047]
机构署名:
本校为第一机构
院系归属:
信息科学与技术学院
摘要:
Image segmentation is a fundamental problem in computer vision. Despite many years of research, general purpose image segmentation is still a very challenging task because segmentation is inherently ill-posed. Among different segmentation schemes, graph theoretical ones have several good features in practical applications. It explicitly organizes the image elements into mathematically sound structures, and makes the formulation of the problem more flexible and the computation more efficient. In this paper, we conduct a systematic survey of graph theoretical methods for image segmentation, where the problem is modeled in terms of partitioning a graph into several sub-graphs such that each of them represents a meaningful object of interest in the image. These methods are categorized into five classes under a uniform notation: the minimal spanning tree based methods, graph cut based methods with cost functions, graph cut based methods on Markov random field models, the shortest path based methods and the other methods that do not belong to any of these classes. We present motivations and detailed technical descriptions for each category of methods. The quantitative evaluation is carried by using five indices - Probabilistic Rand (PR) index, Normalized Probabilistic Rand (NPR) index, Variation of Information (VI), Global Consistency Error (GCE) and Boundary Displacement Error (BDE) - on some representative automatic and interactive segmentation methods. (C) 2012 Elsevier Ltd. All rights reserved.
参考文献:
Andrei D., 2008, IEEE OFC 2008, P1, DOI 10.1109/GLOCOM.2008.ECP.509
Ardon R, 2005, LECT NOTES COMPUT SC, V3757, P520
Ardon R, 2006, INT J COMPUT VISION, V69, P127, DOI 10.1007/sM263-006-6850-z
AUGUSTSO.JG, 1970, J ACM, V17, P571, DOI 10.1145/321607.321608
Bai XF, 2007, IEEE IC COMP COM NET, P1

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