Minimum Spanning Tree Segmentation and Extract with Image Edge Weight Optimization

The unsupervised image segmentation method is sensitive to noise, leading to difficult building image model and poor accuracy of segmentation results. In this paper, a minimum spanning tree segmentation and extract with image edge weight optimization is proposed. Firstly, L0 gradient minimum is used to smooth the noise. The Canny edge detection with Otsu is optimized to obtain more accurate edge information. Secondly, the weight function is redesigned and the weighted graph by using more reasonable color difference space is constructed. The segmentation criterion is improved to optimize the process of object merging and distinguishing. Finally, different types of images are chosen to conduct experiments with noise resistance and segmentation effect. Experimental comparing results show that the proposed algorithm has excellent anti-noise performance, and the segmentation accuracy is improved by 5.15% on average, the over-segmentation rate is decreased by 32.07% on average, and the under-segmentation rate is decreased by 2.69% on average. Moreover, this method is applied to the river and lake extraction of aviation and remote sensing images, and the result has more complete structure, less irrelevant information and better anti-noise performance.