An image matching evolutionary algorithm based on Hu invariant moments

This paper presents an image matching evolutionary algorithm (called IMEA algorithm) based on Hu invariant moments. First, the population is initialized. A group of searched subgraphs is constructed. Second, the fitness function based on Hu invariant moments is designed. The seven Hu invariant moments of the template image and the searched subgraph are calculated. The Euclidean distance of Hu invariant moments is used to measure similarity between the template image and the searched subgraph. The template image and the searched subgraph are matched if these Euclidean distances are less than the set threshold. Finally, a new searched subgraph is constructed by means of a new evolutionary strategy. The new searched subgraph replaces the searched subgraph whose value of the fitness function is maximum. Experimental results demonstrate the great robustness and efficiency of the IMEA algorithm.