融合PCA与混沌自适应遗传算法的图像识别

Image Recognition Based on Principal Component Analysis and Chaos Adaptive Genetic Algorithm

针对图像特征识别转为特征选择优化的问题,提出主成分分析与混沌自适应遗传算法结合的图像目标识别算法。首先通过PCA将图像特征线性组合转变为低维空间几个综合变量;同时改进遗传算法,利用混沌Tent模型生成均匀分布的初始种群、种群交叉及变异概率与种群适应度结合自适应变化,利用类内类间距与特征相关性重新构造适应度函数,采用精英保留策略进行子代选择,得到最优特征子集;最后利用概率神经网络与支持向量机分类器进行训练,识别测试图像。仿真实验表明,PCA与混沌自适应遗传算法结合能降低特征空间维数,使识别性能得到较好提升。

Aiming at the conversion of image feature recognition into feature selection and optimization,a method of image object recognition combined with principal component analysis and chaotic adaptive genetic algorithm is proposed.The algorithm firstly transforms the linear combination of image features into several synthetic variables in low-dimensional space through PCA.At the same time,it improves the genetic algorithm and uses the chaotic Tent model to generate evenly distributed initial populations.The population crossover and mutation probability with population fitness adapt to the change.The fitness function is reconstructed by using the intra-class and inter-class distance and feature correlations.The elite retention strategy is used to select children to obtain the optimal feature subset.The results are trained by using probabilistic neural networks and support vector machine classifiers to test image recognition.Simulation experiments show that the combination of PCA and chaos adaptive genetic algorithm can reduce the dimension of the feature space,and the recognition performance is improved.