Image recognition based on improved LSTM deep neural network

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As the application of electronic equipment in people's production and life is becoming more and more common, digital images have become an indispensable means of information, and massive amounts of image data are being generated every moment. Image recognition is a very important research field in the field of computer vision, and the accurate recognition of objects in images is becoming more and more valuable. The purpose of this paper is to study deep neural network image recognition based on an improved LSTM. The five methods and steps of image recognition, the classification of convolutional neural networks on event images, the method based on LSTM subdivision and the existing event image classification methods are introduced. The experimental part introduces the parameter settings, and gives each visual the experimental results and analysis under the characteristics. Through the comparison of classification accuracy, after LSTM network processing, the event with the highest accuracy rate is 98%, which proves the superiority of the network structure constructed in this paper in the problem of event image recognition, and the classification performance has been improved to a certain extent.