基于区域特征推荐神经网络的数字图像信息识别方法研究

Research on Digital Image Information Recognition Method Based on Areal feature Recommendation Neural Network

现代技术的进步使得图像信息识别系统快速发展。为了提高对数字图像中信息的识别准确率,并保证用户相关数据信息的安全性,实验提出将区域特征推荐网络应用于数字图像信息识别中。过程中首先利用深度学习对图像信息进行了预处理,接着利用区域特征推荐神经网络完成对图像信息的最终识别。结果显示,研究方法在MNIST数据集与Cifar数据集上进行实验,当迭代进行到第20次与第16次时,研究方法有稳定损失函数值0.0151与0.0163。当所有模型的精准率均为0.800时,得到的RNN、TensorFlow-CNN与研究方法的召回率分别为0.722、0.784与0.902。同时研究方法识别图像时,未出现重叠现象。以上结果说明研究方法具有较高的图像识别精准率,能够为后续图像识别系统的性能提升提供一定的参考,具有较高的应用价值。

The advancement of modern technology has led to the rapid development of image information recognition systems.In order to improve the recognition accuracy of information in digital images and ensure the security of user related data information,the experiment proposes to apply the Areal feature recommendation network to digital image information recognition.In the process,the image information is preprocessed by using depth learning,and then the final recognition of image information is completed by using the Areal feature recommendation neural network.The results show that the research method is tested on MNIST data set and Cifar data set.When the iteration reaches the 20th and 16th times,the research method has stable Loss function values of 0.0151 and 0.0163.When the accuracy of all models is 0.800,the recall rates of RNN,TensorFlow CNN,and research methods obtained are 0.722,0.784,and 0.902,respectively.When studying methods for image recognition simultaneously,there was no overlap phenomenon.The above results indicate that the research method has a high accuracy in image recognition,which can provide a certain reference for the performance improvement of subsequent image recognition systems and has high application value.