Semi-supervised Image Segmentation Based on K-means Algorithm and Random Walk

被引：0

作者：

Cai Xiumei ^{[1
]}

Bian Jingwei ^{[1
]}

Wang Yan ^{[1
]}

Cui Qiaoqiao ^{[1
]}

机构：

[1] Xian Univ Posts & Telecommun, Sch Automat, Xian 710121, Peoples R China

来源：

2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) | 2019年

关键词：

k-means; image segmentation; random walk; semi-supervised;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Semi-supervised image segmentation is a process of classifying unlabeled pixels using known labeling information. In order to realize image segmentation, solve the problem of setting a large number of seed points in the random walk algorithm, and solve the local optimization problem in the K-means algorithm, this paper proposes a semi-supervised image segmentation algorithm based on the K-means algorithm and random walk. Firstly, the K-means algorithm is used for clustering to determine the clustering center, then, the transfer probability from each unlabeled pixel to the seed point is calculated based on the random walk algorithm, and the image segmentation is completed according to the transfer probability. It can be seen from the experimental results that the segmentation accuracy is greatly improved and the effectiveness of this paper is verified.

引用

页码：2853 / 2856

页数：4

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