摘要
GP(genetic programming)算法,常被称为遗传规划、遗传编程或遗传程序设计,是一种进化计算算法,能够利用计算机技术自动生成"程序"(模型)解决实际问题.介绍了GP算法基本原理、算法目前发展及其主要应用领域.对GP算法在图像分析领域如特征提取、图像分类、边缘检测、图像分割等的代表性研究工作进行了较为系统且全面的讨论和综述.最后,对GP算法在图像分析上的研究难点及热点如计算复杂、提高泛化能力、迁移学习等进行了总结和归纳,指出了未来主要研究方向.
As an evolutionary computation (EC)technique,Genetic programming (GP)has been widely applied to image analysis in recent decades.However,there was no comprehensive and systematic literature review in this area.To provide guidelines for the state-of-the-art research,this paper presentd a survey ofthe literature in recent years on GP for image analysis,including feature extraction,image classification,edge detection,and image segmentation.In addition,this paper summarised the current issues and challenges,such as computationally expensive,generalisation ability and transfer learning,on GP for image analysis,and pointd out promising research directions for future work.
作者
毕莹
薛冰
张孟杰
BI Ying;XUE Bing;ZHANG Mengjie(School of Engineering and Computer Science,Victoria University of Wellington,Wellington 6140,New Zealand)
出处
《郑州大学学报（工学版）》
CAS
北大核心
2018年第6期3-13,共11页
Journal of Zhengzhou University（Engineering Science）
关键词
GP
遗传规划
遗传编程
图像分析
进化计算
特征提取
图像分类
边缘检测
图像分割
genetic programming
image analysis
evolutionary computation
feature extraction
image classi- fiCation
edge detection
image segmentation
