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基于微粒群优化的图像分类方法研究
消耗积分:3 |
格式:rar |
大小:162 |
2009-06-10
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论文提出了一种新的图象分类算法——基于微粒群的图象分类算法。将此算法和K 均值聚类算法分别应用于MRI 人脑图象的分类，并进行了比较。实验结果表明：基于微粒群的图象分类算法具有较好的全局收敛性，不仅能有效克服K 均值算法易陷入局部极小
值的缺点，而且全局收敛性能优于K 均值算法。
关键词:微粒群算法；K 均值算法；图象分类
Research of Image Classification Method Based on Particle Swarm Optimization Zhou Xian-cheng （Department of Computer Science and Electronic Engineering Hunan Business College， Changsha Hunan 410205,China） [ABSTRACT]:This paper proposes a new image classification algorithm based on Particle Swarm Optimization.The new image classification algorithm has been applied to MRI images to illustrate its applicability.The experimental results show that the proposed algorithm not only avoids the local optima,but also has greater searching capability than K-means algorithm,yielding promising results.
[Key Words]: Particle swarm optimization; K-means algorithm; Image classification
值的缺点，而且全局收敛性能优于K 均值算法。
关键词:微粒群算法；K 均值算法；图象分类
Research of Image Classification Method Based on Particle Swarm Optimization Zhou Xian-cheng （Department of Computer Science and Electronic Engineering Hunan Business College， Changsha Hunan 410205,China） [ABSTRACT]:This paper proposes a new image classification algorithm based on Particle Swarm Optimization.The new image classification algorithm has been applied to MRI images to illustrate its applicability.The experimental results show that the proposed algorithm not only avoids the local optima,but also has greater searching capability than K-means algorithm,yielding promising results.
[Key Words]: Particle swarm optimization; K-means algorithm; Image classification
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