摘要
在进行图像区域分割时,为了减少过度分割现象,可利用K均值算法简单、快速并且能够有效地处理大数据库的优点及概率松弛算法并行快速且考虑空间信息的优点,同时考虑灰度信息和空间信息将两种方法相结合应用于图像的区域分割。首先利用K均值聚类方法将图像初步分为多个类,然后,利用迭代的概率松弛法对粗分结果进行优化,对一些疑似像素进行进一步分割和目标提取。实验结果表明,该算法比较简单且具有良好的特性。
In order to reduce over- segmentation, ean take advantages of K- means which is simple, fast and able to deal with large database and probability relaxation. The method of combining K - means and probability relaxation is used in this paper. First apply K - means clustering method to segment the image pixels into different regiments. Then an iterative probability relaxation operation is applied in order to optimize the coarse segmentation to further segment the uncertain pixels according to their statistie properties. Experimental results indicate that proposed method is effective for image segmentation and object extraction.
出处
《计算机技术与发展》
2010年第2期68-70,74,共4页
Computer Technology and Development
基金
广东省攻关项目(2008B080701053)
关键词
图像区域分割
K均值
概率松弛
region- based image segmentation
K - means
probability relaxation
