Conventional image matching algorithms based on mutual information not only consume large amounts of time

but also ignore the pixels’ utilities and spatial relations. In this paper

a novel image matching algorithm using multilevel features is proposed based on quantitative-qualitative measure of mutual information(Q-MI). Firstly

multilevel features are extracted on the edge image

including edge points of interest

edge points and edge neighborhood points. Secondly

according to the characteristics of multilevel features

the Q-MI joint utility for each pixel value pair is computed. Lastly

an optimizer based genetic algorithm(GA) is applied to effectively search the best matching transformation parameters

with Q-MI as the fitness function. Experimental results demonstrate the accuracy

efficiency and robustness of this algorithm.