Machine Vision Attention Selection Algorithm Based on Artificial Intelligence Image Recognition

This paper focuses on the field of artificial intelligence image recognition and aims to develop an efficient machine vision attention selection algorithm. By combining theoretical analysis with experimental verification, this study deeply analyzes the advantages and disadvantages of existing algorithms and proposes an innovative machine vision attention selection algorithm based on artificial intelligence image recognition. An experimental platform is built and specific datasets are used for testing. The main findings show that the new algorithm performs excellently in key performance indicators such as accuracy and recall rate. Compared with traditional algorithms, this algorithm innovatively integrates multi-modal features, significantly improving the ability to capture key information in complex image scenes and effectively reducing the misjudgment rate. The research conclusion confirms that this algorithm demonstrates good adaptability and high efficiency in machine vision tasks, providing strong support for the application of artificial intelligence image recognition technology in more fields.