Clustering Color Image Segmentation Based on Maximum Entropy

摘要：

　　Maximum entropy is meaningful for representing pixels spatial distribution in the image.This paper proposed a new clustering segmentation approach for color image according to the maximum entropy.Firstly,quantize the HSV color space to equal intervals.The probability distribution of pixels in the quantized space can be seen as a random process.Select a slide interval on the histogram to estimate the classes based on the maximum entropy in the color space.Then observed class number and the initial cluster center.Segmented pixels in to regions by clustering and used spatial filtering to eliminate meaningless regional and holes.The experiment results has shown that this algorithm achieved a good segmentation.

关键词： Color image Segmentation Maximum entropy theory K-means clustering

作者: Sima Haifeng Liu Lanlan

作者单位: School of Computer Science and Technology ,Henan Polytechnic University School of Computer Science a School of Emergence Management Henan Polytechnic University

会议类型: 国际会议

会议名称: 2012 2nd International Conference on Computer Application and System Modeling(2012第二届计算机应用与系统建模国际会议)(ICCASM-2012)

会议地点: 沈阳

会议语种:英文

页码: 1466-1468

在线出版日期: 2012-07-27（万方平台首次上网日期，不代表论文的发表时间）