Efficient digital camouflage pattern generation algorithm based on improved GA-K-means clustering algorithm

Abstract

Efficient algorithms for generating digital camouflage patterns are widely used in the fields of smart windows, electronic skin, and bionic electronics. In this paper, a digital camouflage pattern generation algorithm based on an improved GA-Kmeans clustering algorithm was proposed. The proposed algorithm renders accurately extraction of the background domain colors while maintaining the background textures. The key features of our algorithm include: (1) a rectangle blocks segmentation and random scrambling algorithm which works fast and ensures maximum retention of local texture details is designed to process the neighborhoond background image and disorder the background textures, (2) an improved GA-K-means clustering algorithm which works as a global search algorithm to search for the global optimal clustering centers is designed to accurately cluster the camouflage colors, and (3) the super-pixel algorithm which works efficiently to eliminate the noise and texture boundaries of images is used to smooth the image after color clustering. Compared to four typical algorithms, the proposed algorithm generates camouflage patterns that improve the color similarity (CSIM) by 8.3% to 38.2%, the texture similarity (TSIM) by 9.9% to 35.1%, and the edge contour similarity (ESIM) by 6.9% to 37.5%.