A novel active contour model based on modified symmetric cross entropy for remote sensing river image segmentation. (July 2017)

• Record Type: Journal Article
• Authors: Han, Bin; Wu, Yiquan
• Is Part Of: Pattern recognition. Volume 67(2017:Jul.)
• Page Start: 396
• Page End: 409
• Publication Date: 2017-07
• Subjects: Image segmentation -- Remote sensing river image -- Active contour model -- Modified symmetric cross entropy -- Chebyshev distance; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.02.022
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The external energy constraint terms of our model are defined by the perfect symmetric cross entropy instead of the cross entropy, which describes the differences of pixel grayscale values inside the object and background regions much more accurately. The medians of pixel grayscale values inside the object and background regions are selected as the region fitting centers instead of means, which can represent the pixel grayscale values better. The constant region energy weight is replaced by the Chebyshev distance between the pixel grayscale values inside the region and its region fitting center, which can be adaptively adjusted. The state-of-the-art active contour models cannot segment the remote sensing river images accurately. While our model can segment them more accurately and rapidly, which has the obvious advantages in both segmentation performance and segmentation efficiency. Abstract: The traditional active contour models cannot segment the remote sensing river images accurately. To solve this problem, a novel active contour model based on modified symmetric cross entropy is proposed. In the proposed model, the external energy constraint terms are defined by the symmetric cross entropy and the region fitting centers are represented by the medians of pixel grayscale values inside the object and background regions. Moreover, the penalty energy term is incorporated into the energy functional to avoid the re-initialization. In order to improve the segmentation efficiency of the proposed model, the Chebyshev distance between the pixel grayscale values inside the region and its region fitting center is chosen as its region energy weight, which can be adaptively adjusted, instead of the constant region energy weight. The extensive experiments are performed on a large number of remote sensing river images and the results demonstrate that, compared with the CV model, the GAC model, the CEACM model, the RSF model, the LIF model, and the LGIF model, the proposed model can segment the images more accurately and rapidly, which has the clear advantages in both segmentation performance and segmentation efficiency.