Remote sensing image object extraction using convex geometric active contour model

Authors:
Ning He;Lulu Zhang;Yixue Wang
Affiliations:
Beijing Union University, Beijing, China;Beijing Union University, Beijing, China;Shenyang Institute of Engineering, Shenyang, China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

The geometric active contour model is a popular method for computing the segmentation of an image into two phases, based on Mumford-Shah model. The main problem in image segmentation based this method may lead to non-convex minimization problems that it difficult to obtain a global solution. In this paper, we propose a convex relaxation of the popular K-means algorithm. Our approach is based on the vector-valued relaxation technique developed by Brown et al. (UCLA CAM Report 10-43, 2010) and Goldstein et al. (UCLA CAM Report 09-77, 2009). We applied the proposed framework to multi-object extraction problems on remote sensing images. We provide several experimental results to demonstrate that our convex model yields global solutions to the well known Mumford-Shah model.