Multi-threshold Segmentation for Color Image Based on Pyramid Evolution Strategy

Abstract

In view of the fact that traditional intelligent optimization algorithms for multi-threshold segmentation of color images fall to consider the competition and cooperation between populations,which results in local optimization problems that affect the segmentation effect.In order to improve the segmentation effect,an improved pyramid evolution strategy (IPES) is proposed.The algorithm designs an adaptive search operator suitable for the multi-threshold segmentation problem of color images;expands the search space at all levels,improves the optimization ability of the algorithm;takes Otsu as the optimization goal and uses the competition and cooperation relationship between populations to solve the local optimization problem,thereby improving the accuracy of the solution and the effect of segmentation.The performance of IPES is tested on existing standard test images and compared with other eight algorithms.Experimental results show that the peak signal-to-noise ratio of the image segmented by IPES algorithm is between 28~35 dB,which is at least 10 dB higher than that of the improved tree-seed algorithm and traditional particle swarm algorithm and differential evolution algorithm;the structural similarity is between 89%~97%,increased by at least 3%.The image quality after segmentation is better and the structural similarity is higher.Therefore,the algorithm has good perfor-mance in solving multi-threshold segmentation problem of color images.

Keywords

• color image|multi-threshold segmentation|pyramid evolution strategy|otsu|particle swarm algorithm