| 基于NW小世界邻居的粒子群多阈值分割算法 |
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| 引用本文： | 陈自郁,何中市,张程.基于NW小世界邻居的粒子群多阈值分割算法[J].计算机科学,2009,36(3):201-204. |
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| 作者姓名： | 陈自郁 何中市 张程 |
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| 作者单位： | 重庆大学计算机学院,重庆,400044 |
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| 基金项目： | 国家高技术研究发展计划(863计划)，重庆市自然科学基金，重庆大学研究生科技创新项目 |
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| 摘 要： | 针对多阈值分割问题,提出了一种新的多阈值分割算法.此算法采用相对类内方差代替传统Otsu算法中的绝对类内方差,改善了传统Otsu对小对象分割不理想的弱点;采用NW小世界模型作为粒子群优化的社会认知拓扑结构,具有较好的全局寻优能力和较快的收敛速度.实验结果显示此算法具有好的性能.
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| 关 键 词： | NW小世界模型 粒子群优化 多阈值分割 类方差 |
| 收稿时间： | 2008/8/11 0:00:00 |
Image Multilevel Thresholding Based on PSO with NW Small World Neighborhood |
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| CHEN Zi-yu,HE Zhong-shi,ZHANG Cheng.Image Multilevel Thresholding Based on PSO with NW Small World Neighborhood[J].Computer Science,2009,36(3):201-204. |
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| Authors: | CHEN Zi-yu HE Zhong-shi ZHANG Cheng |
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| Affiliation: | College of Computer Science;Chongqing University;Chongqing 400044;China |
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| Abstract: | As for image multilevel thresholding,a novel algorithm was proposed.Adopting relative intra-class variance instead of absolute intra-class variance,the algorithm improved the segmentation result for small objects with Otsu method.Introducing NW small world model into particle swarm optimization,the algorithm gained better optimization performance.Experimental results show that the proposed algorithm was promising and outperformed some existing techniques. |
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