Research on Volleyball Image Classification Based on Artificial Intelligence and SIFT Algorithm

Abstract

Due to the application scenarios of image matching, different scenarios have different requirements for matching performance. Faced with this situation, people cannot accurately and timely find the information they need. Therefore, the research of image classification technology is very important. Image classification technology is one of the important research directions of computer vision and pattern recognition, but there are still few researches on volleyball image classification. The selected databases are the general database ImageNet library and COCO library. First, the color image is converted into a gray image through gray scale transformation, and then the scale space theory is integrated into the image feature point extraction process through the SIFT algorithm. Extract local feature points from the volleyball image, and then combine them with the Random Sample Consensus (RANSAC) algorithm to eliminate the resulting mismatch. Analyze the characteristic data to obtain the data that best reflects the image characteristics, and use the data to classify existing volleyball images. The algorithm can effectively reduce the amount of data and has high classification performance. It aims to improve the accuracy of image matching or reduce the time cost. This research has very important use value in practical applications.