期刊
NEUROCOMPUTING
卷 87, 期 -, 页码 90-98出版社
ELSEVIER
DOI: 10.1016/j.neucom.2012.02.008
关键词
Quantum evolutionary clustering algorithm; Watershed algorithm; SAR image segmentation
资金
- National Natural Science Foundation of China [60703108, 61003199, 61001202]
- Provincial Natural Science Foundation of Shaanxi of China [2011JQ8020, 2010JQ8023]
- China Postdoctoral Science Foundation [20090451369, 20090461283, 200801426, 201104618]
- Fundamental Research Funds for the Central Universities [K50511020014, K50511020011, K50510020011]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
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.
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