计算机工程与应用2016，Vol.52Issue(21)：195-201,217,8.DOI:10.3778/j.issn.1002-8331.1501-0003
基于噪音受益的快速图像分割算法
Fast image segmentation algorithm based on noise benefit
牛艺蓉 1王士同1
作者信息
- 1. 江南大学 数字媒体学院，江苏 无锡 214122
- 折叠
摘要
Abstract
Image segmentation denotes a process by which a raw image is partitioned into nonoverlapping regions. When using the existing improved Gaussian mixture model in image segmentation, how to speed up its segmentation process is a significant research topic. Based on the latest noise-benefit EM algorithm, this paper speeds up the convergence speed of the existing improved Gaussian mixture model by adding artificial noise, which achieves the goal of speeding up image segmentation. Additive noise speeds up the average convergence of the EM algorithm to a local maximum of the likeli-hood surface when adding noise to meet the noise-benefit EM theorem. Improved Gaussian mixture model is a special case of the expectation-maximization algorithm, therefore, noise-benefit EM theorem applies to improved gaussian mix-ture model. Experimental results indicate that the algorithm speeds up the convergence speed when it is used for image segmentation, and the time complexity is decreased significantly.关键词
噪声受益/新型期望最大化算法(NEM)定理/图像分割/空间邻域关系/改进的高斯混合模型Key words
noise benefit/New Expectation Maximization(NEM)theorem/image segmentation/spatial neighborhood relationships/improved Gaussian mixture model分类
信息技术与安全科学引用本文复制引用
牛艺蓉,王士同..基于噪音受益的快速图像分割算法[J].计算机工程与应用,2016,52(21):195-201,217,8.
