摘要
当前图像识别存在一定的缺陷,如识别准确率低、耗时长,为了提高图像识别的准确率,提升图像识别效率,设计了基于人工智能技术的图像识别模型。采集图像纹理特征、颜色特征,利用人工智能技术中的支持向量机构建识别模型,将所提取的图像纹理特征、颜色特征作为模型输入,最终输出图像识别结果,并采用自适应的混合粒子群算法优化图像特征选择机制与支持向量机参数,引入线性加权多目标函数提升图像识别效果。图像识别仿真实验结果表明,该模型能够较为完整地提取图像的特征,同时准确划分图像类别,获得了理想的图像识别结果。
There are certain shortcomings in current image recognition,such as low recognition accuracy and long time consumption.In order to improve the accuracy and efficiency of image recognition,an image recognition model based on artificial intelligence technology is designed.Image texture features,color features are collected,and a recognition model is constructed using Support Vector Machines in artificial intelligence technology.The extracted image texture features,color features are used as model inputs,and the final output of image recognition results is obtained.The adaptive hybrid particle swarm optimization algorithm is used to optimize the image feature selection mechanism and support vector machine parameters,and a linear weighted multi-objective function is introduced to improve the recognition effect of images.Image recognition simulation experiments are conducted,and the results show that the model can extract the features of images more completely,accurately classify images,and obtain ideal image recognition results.
作者
张旭
王宁
刁士军
吕成
王鹏辉
ZHANG Xu;WANG Ning;DIAO Shijun;LV Cheng;WANG Penghui(CHN Energy Ningxia Heating Co.,Ltd.,Yinchuan 750000,China)
出处
《电子设计工程》
2025年第20期145-149,共5页
Electronic Design Engineering
关键词
识别准确率
识别效率
人工智能
特征提取
支持向量机
recognition accuracy
identification efficiency
AI
feature extraction
Support Vector Machine
