Image Extraction Based on Machine Learning and Image Recognition and Analysis Technology

Abstract

In recent years, with the rapid development of the Internet and mobile Internet, the image data on the Internet has shown explosive growth. Image data has simple and intuitive characteristics and contains a large amount of information, and is widely used as an information carrier. With the development of artificial intelligence in recent years, machine learning has also risen rapidly, and image recognition technology occupies a very important position in machine learning. This article first analyzes machine learning and image recognition through literature research methods and quantitative analysis methods, and then uses different image recognition and analysis technologies based on the big data platform gallery to test the accuracy of the technology, and provide research value for the application of image recognition and analysis technologies. The experimental results show that in the image recognition based on artificial neural network, in the case of the same number of levels and different node numbers, the recognition rate of the picture first increases, then reaches the peak, and then decreases. The overall rate of return is a trend that first rises and then declines. It can be seen that when the data is 450, that is, the number of nodes is set to half of the input data, and the artificial neural network has the highest recognition rate, the data has the best performance. In the image recognition based on convolutional neural network, the recognition rate of LeNet-5 in the CIFAR-10 image library was only 93 at the beginning, and the latter two models were 95 and 96 respectively at the beginning. CNN1 only achieves 30 times the stability, while the improved CNN2 and CNN3 models achieve a relatively stable recognition rate of nearly 20 times, thereby increasing the relative learning speed. In the end, the recognition rate of the CNN1 model reached 97.8, and the improved CNN1 model achieved a maximum recognition rate of 97.82. The model can reach 98.8 and 99.5, and the improved model algorithm significantly improves the image recognition rate