摘要
针对传统的目标识别方法存在易陷入局部最佳值和识别精度低的问题。提出基于遗传算法优化神经网络的图像目标识别方法,通过灰度共生矩阵运算出图像的纹理特征值,并融合像素灰度值构成分类图像的特征矢量,将特征矢量输入到神经网络中实施训练。神经网络先采用遗传算法获取最佳检索范围,再通过高阶神经网络实施寻优运算,获取最佳的图像目标识别结果。实验结果说明,所提方法在图像目标识别精度和效率方面具有较高的优越性。
For the traditional target recognition methods are easy to fall into local optimum value and have low recognitionaccuracy,an image target recognition method based on genetic algorithm optimizing neural network is proposed.The texture ei?genvalues of the image are calculated by means of gray?level co?occurrence matrix(GLCM),and fused with the pixel grey?levelvalue to form the feature vector of the classification image.The feature vector is input into neural network for training.The genet?ic algorithm adopted in neural network is used to get the best search range,and then the optimization operation is performed inhigh?order neural network to get the best image target recognition results.The experimental results show that the proposed meth?od has high superiority in the aspects of image target recognition accuracy and efficiency.
作者
李隽
王伟
LI Jun;WANG Wei(Neijiang Vocational and Technical College,Neijiang 611002,China;Chengdu College,University of Electronic Science and Technology of China,Chengdu 610000,China)
出处
《现代电子技术》
北大核心
2017年第20期111-113,共3页
Modern Electronics Technique
基金
国家自然科学基金项目(61006027)
关键词
遗传算法
特征矢量构成
神经网络
图像目标识别
genetic algorithm
feature vector constitution
neural network
image target recognition
作者简介
李隽（1981—），女，四川内江人，讲师。研究方向为计算机应用、软件工程。;王伟（1981—），女，四川成都人，讲师，硕士。研究方向为计算机软件设计、计算机科学。
