Image matching algorithm of multilevel features based on quantitative-qualitative measure of mutual information
Cheng Yong-mei · 2010
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.