A Novel Image Segmentation Algorithm Based on Graph Cut Optimization Problem
Abstract: Image segmentation,
a fundamental task in computer vision, has been widely used in recent years in
many fields. Dealing with the graph cut optimization problem obtains the image
segmentationresults. In this study, a novel algorithm with weighted graphs was
constructed to solve the imagesegmentation problem through minimization of an
energy function. A binary vector of the segmentationlabel was defined to
describe both the foreground and the background of an image. To demonstrate theeffectiveness
of our proposed method, four various types of images were used to construct a
series ofexperiments. Experimental results indicate that compared with other
methods, the proposed algorithm can effectively promote the quality of image
segmentation under three performance evaluation metrics, namely,
misclassification error rate, rate of the number of background pixels, and the
ratio of the number of wrongly classified foreground pixels.
Author: Zhang Guang-hua, Xiong
Zhong-yang, Li Kuan, Xing Chang-yuan, Xia Shu-yin
Journal Code: jptkomputergg150048
