Adaptive Adjustment of Feature Weight Coefficients Based on Genetic Algorithm in Image Retrieval
Zhihui Wang, Xiaoli Ge, Jinlin Li, Qila Sa, Wenbo Xu, Yuejiao Fan, Yiqun Zhao · 2016
It is worth studying that how to utilize image features so as to achieve the satisfied result of CBIR (content-based image retrieval).To solve this problem, this paper proposes an adaptive image retrieval algorithm based on color feature and texture feature, in which the two kinds of features are combined and the weight coefficients of them are determined with genetic algorithm.Genetic algorithm, starting from solving practical problems, constructs an initial population with the potential solutions of practical problems.In the proposed algorithm, firstly, the initial population consisted with the weight coefficients of texture features (or color ones) is randomly generated; then, selection, crossover, and mutation are operated so that each new generation of population is gradually closed to the optimal solution; finally, the adaptively adjusted weight coefficients are obtained.Experimental results show that when fusing color feature and texture feature in image retrieval, the introduction of genetic algorithm, by which determine the weight coefficients of two kinds of features, makes both the recall ration and precision ratio of retrieval are improved.The proposed algorithm of image retrieval combined with genetic algorithm can automatically set the optimal weights of the image features according to the different image to be retrieved submitted by users, and can basically achieve the ideal suitable weights and output the relative ideal retrieval results.