02314nas a2200397   4500000000100000000000100001008004100002260005900043653001000102653002700112653001700139653003000156653002200186653002300208653001800231653002000249653001600269653001700285653004400302653002800346653001700374653002800391653001800419653002000437653002600457653003000483653003300513100001600546700001500562700001500577245012400592856015500716300001000871520101000881020002501891       2021                            d  bInstitute of Electrical and Electronics Engineers Inc.10aColor10aColor image processing10aColour image10aColour image segmentation10aImage enhancement10aImage segmentation10aKapur entropy10aKapur s entropy10aLevy flight10aLevy flights10aMuli-threshold color image segmentation10aMultilevel thresholding10aOptimization10aOptimization algorithms10aRenyi entropy10aRenyi s entropy10aSignal to noise ratio10aThresholding segmentation10aWhale optimization algorithm1 aX.-W. Cheng1 aH.-Q. Wang1 aG.-C. Chen00aAn improved whale optimization algorithm for dinosaur lantern festival color image multilevel thresholding segmentation  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85117961924&doi=10.1109%2fPRAI53619.2021.9551031&partnerID=40&md5=6d31e76559a3c59c161ae86b8397dcf6  a28-343 aThe whale optimization algorithm has the shortcomings of precocity and insufficient diversity when it is used to segment the color lantern image. A chaotic levy whale optimization algorithm is proposed to search the optimal Kapur s entropy and Renyi entropy. In this algorithm, particles are used to represent a feasible one-dimensional multi threshold vector, and chaos initialization strategy is adopted to improve the randomness of the algorithm. Based on the whale optimization algorithm, Levy flight strategy is used to enhance the diversity and global search ability of the algorithm. The simulation results show that the proposed algorithm is better than the other five algorithms. In addition, the algorithm using Renyi entropy has better performance than Kapur s entropy in terms of Signal to Noise Ratio (PSNR), Feature Similarity index (FSIM), and best fitness. It also provides a reference for the application of swarm intelligence algorithm in intangible cultural heritage image segmentation.  a9781665413220 (ISBN)