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Two-Stage Image Segmentation Scheme Based on Inexact Alternating Direction Method

Part of: Numerical analysis in abstract spaces

Published online by Cambridge University Press:  20 July 2016

Zhanjiang Zhi ,
Yi Sun  and
Zhi-Feng Pang
Zhanjiang Zhi*
Affiliation:
School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China School of Mathematics and Statistics, Henan University, Kaifeng, 475004, China
Yi Sun*
Affiliation:
School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China
Zhi-Feng Pang*
Affiliation:
School of Mathematics and Statistics, Henan University, Kaifeng, 475004, China
*
*Corresponding author. Email addresses:zhifengpang@163.com (Z.-F. Pang), zhizhangjiang@163.com (Z.-J. Zhi), lslwf@dlut.edu.cn (Y. Sun)
*Corresponding author. Email addresses:zhifengpang@163.com (Z.-F. Pang), zhizhangjiang@163.com (Z.-J. Zhi), lslwf@dlut.edu.cn (Y. Sun)
*Corresponding author. Email addresses:zhifengpang@163.com (Z.-F. Pang), zhizhangjiang@163.com (Z.-J. Zhi), lslwf@dlut.edu.cn (Y. Sun)
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Abstract

Image segmentation is a fundamental problem in both image processing and computer vision with numerous applications. In this paper, we propose a two-stage image segmentation scheme based on inexact alternating direction method. Specifically, we first solve the convex variant of the Mumford-Shah model to get the smooth solution, the segmentation are then obtained by apply the K-means clustering method to the solution. Some numerical comparisons are arranged to show the effectiveness of our proposed schemes by segmenting many kinds of images such as artificial images, natural images, and brain MRI images.

Keywords

Mumford-Shah modelTotal variationImage segmentationK-meansInexact alternating direction method

MSC classification

Secondary: 65J22: Inverse problems 68U10: Image processing 90C25: Convex programming
Type
Research Article
Information
Numerical Mathematics: Theory, Methods and Applications , Volume 9 , Issue 3 , August 2016 , pp. 451 - 469
Copyright
Copyright © Global-Science Press 2016 

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