Quantum evolutionary clustering algorithm based on watershed applied to SAR image segmentation

被引：33
|
作者：
Li, Yangyang [1 ]
Shi, Hongzhu [1 ]
Jiao, Licheng [1 ]
Liu, Ruochen [1 ]
机构：
[1] Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China
来源：
NEUROCOMPUTING | 2012年 / 87卷
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Quantum evolutionary clustering algorithm; Watershed algorithm; SAR image segmentation;
D O I：
10.1016/j.neucom.2012.02.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The goal of segmentation is to partition an image into disjoint regions. In this paper, the segmentation problem based on partition clustering is viewed as a combinatorial optimization problem. A new algorithm called a quantum evolutionary clustering algorithm based on watershed (QWC) is proposed. In the new algorithm, the original image is first partitioned into small pieces by watershed algorithm, and the quantum-inspired evolutionary algorithm is used to search the optimal clustering center, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with QICW, the genetic clustering algorithm based on watershed (W-GAC) and K-means algorithm based on watershed (W-KM). (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：90 / 98
页数：9
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