Complex background image segmentation based on multi-scale features

Aiming at the problems of large feature extraction error and poor segmentation effect in complex background image segmentation, a complex background image segmentation algorithm based on multi-scale features is designed. Firstly, the kernel function of multi-scale extraction method is used to initially determine the image feature density, and the Gaussian kernel function is introduced to determine the distance between the feature distribution points and the centre point to complete the image global feature extraction; then, set the grey level constraint of the local feature image to complete the local feature extraction; finally, determine the image edge threshold, divide the complex pixel feature area, determine the image feature membership and fuzzy rate, transform the segmentation problem into a nonlinear problem, and complete the segmentation. The experimental results show that the proposed algorithm can reduce the feature extraction error, have a maximum error of less than 1%, and optimise the image segmentation results.