Image Recognition Algorithm Based on Artificial Intelligence and Machine Learning

Abstract

At present, most of the existing image recognition methods are not only cumbersome in process but also require manual design of functions, resulting in poor recognition results and time-consuming training. This study explores image recognition algorithms based on artificial intelligence and machine learning, which can simulate the hierarchical structure of the human brain and nervous system, realize automatic extraction of complex features, and have powerful data representation capabilities. In this study, the structure of artificial neuron is introduced first, and the training process of the neural network is introduced in detail. At the same time, this study improves the traditional training algorithm and proposes two new machine learning models for different application scenarios, which effectively improves the performance of the optimal model. In relevant tests, the effect is significant. On the basis of these improved algorithms, an online recognition system is designed and implemented. The recognition accuracy rate of the system for the hidden layer reaches more than 90%, which verifies the effectiveness of the technology. At the same time, after repeated experiments, the results show that the multiview algorithm effectively solves the problem that the recognition results of the traditional multiview algorithm are affected by the size of the target contour.