西安电子科技大学学报（自然科学版）2017，Vol.44Issue(1)：159-164,6.DOI:10.3969/j.issn.1001-2400.2017.01.028
结合高阶图模型与蚁群优化的图像匹配方法
Second-order graph model ant and colony optimization based image matching
摘要
Abstract
Image matching is a fundamental problem in the computer vision field . This paper focuses on image matching based on the graph structure model . The methods of the graph model establishment in the second‐order or high‐order constraint are studied . In order to overcome the defects of traditional optimal algorithms which fall easily into the local optimal solution , this paper adopts the ant colony optimization algorithm to optimize the match score function and proposes an high‐order graph matching algorithm based on ant colony optimization . It first applies the tensor matching algorithm to initialize the pheromone matrix to provide a good start point , adopts the affinity tensor to provide the priori knowledge for computing the heuristic factor , then calculates the transition probability using the pheromone and heuristic factor , and finally updates the pheromone in two ways by the solutions which have been searched . The two updating rules of pheromone are local and global . Experimental results show that this algorithm can get a higher matching accuracy and has a stronger robustness against deformation noises and outliers compared with others .关键词
图像匹配/蚁群算法/高阶图匹配/优化Key words
image matching/ant colony algorithm/high-order graph matching/optimization分类
信息技术与安全科学引用本文复制引用
杨思燕,曹文灿,李世平..结合高阶图模型与蚁群优化的图像匹配方法[J].西安电子科技大学学报（自然科学版）,2017,44(1):159-164,6.基金项目
国家自然科学基金资助项目（61272280）；大数据环境下计算机类课程 MOOC 研究资助项目（15G-04-A04）；大数据下的计算机类课程资源建设实践研究资助项目 （）
