Research of Machine Learning-Based Image Recognition Technology

Abstract

With the rapid advancement of artificial intelligence technology, machine learning-based network image recognition has made significant strides in computer vision systems of most countries, impacting both production and daily life. Building on this progress, a computer network image recognition system has been developed using machine learning techniques that focus on image features such as crossverification, fitting, accuracy, feature selection, and dimensionality reduction. The findings indicate that machine learning algorithms enhance the stability and multi-domain applicability of computer image recognition through cross verification and adjustments in decision tree units. Additionally, recognition technologies that utilize feature vector extraction, edge information, and texture characteristics from machine learning algorithms contribute to improved accuracy in image recognition.