混合模拟退火与蚁狮优化的图像匹配方法

doi:10.11896/j.issn.1002-137X.2019.06.050

摘要/Abstract

摘要： 针对传统群优化算法在图像匹配中存在匹配效率低、匹配精度不高的问题,提出一种混合模拟退火(Simulated Annealing,SA)与蚁狮优化(Ant Lion Optimizer,ALO)的图像匹配方法。该方法首次将ALO算法应用到图像匹配中,利用边界收缩机制和蚂蚁与蚁狮之间的相互作用的搜索方式,来提高匹配效率和匹配精度;然后采用局部嵌入准则进行评估,若匹配结果陷入局部最优则引入改进模拟退火机制,通过Lévy飞行进行位置扰动更新以及通过Metropolis准则使其跳出局部嵌入问题,增强算法的寻优性能,提高匹配精度;否则直接通过ALO搜索策略完成图像匹配。实验结果表明,该方法具有匹配速度快、匹配精度高的特点。

关键词: ALO, 模拟退火, 群优化, 图像匹配

Abstract: Aiming at low matching efficiency and accuracy of traditional swarm optimization algorithms in image matching,this paper proposed an image matching method combining hybrid simulated annealing(SA) and ant lion optimizer(ALO).In this method,the ALO algorithm is applied to image matching for the first time,and the boundary shrinkage mechanism and the search method of the interaction between ant and antlion are exploited to improve the matching efficiency and accuracy.Then,on the basis of making use of the rule of partial embedding criterion,the simulated annealing mechanism is introduced if the matching result falls into local optimum.Besides,the Lévy flight and the Metropolis criterion are utilized to ensure the algorithm run beyond the local optimum,thus improving the optimization performance and matching accuracy.Otherwise,ALO search strategy is directly used to complete image matching.The experimental results demonstrate fast matching speed and high matching accuracy of the proposed method.

Key words: Ant lion optimizer, Image matching, Simulated annealing, Swarm optimization

中图分类号:

TP391.41

张焕龙, 高增, 张秀娇, 史坤峰. 混合模拟退火与蚁狮优化的图像匹配方法[J]. 计算机科学, 2019, 46(6): 328-333. https://doi.org/10.11896/j.issn.1002-137X.2019.06.050

ZHANG Huan-long, GAO Zeng, ZHANG Xiu-jiao, SHI Kun-feng. Image Matching Method Combining Hybrid Simulated Annealing and Antlion Optimizer[J]. Computer Science, 2019, 46(6): 328-333. https://doi.org/10.11896/j.issn.1002-137X.2019.06.050

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