Image Segmentation Technology of Marathon Motion Video Based on Machine Learning

Qiang, Huang; Yi-de, Liao

In order to improve that segmentation quality of the video image of the marathon, a video image segmentation algorithm based on machine learning is proposed. Constructing the edge contour feature detection and the pixel feature point fusion reconstruction model of the marathon moving video image, carrying out multi-level feature decomposition and gray pixel feature separation of the marathon moving video image, and establishing a visual feature reconstruction model of the marathon moving video image, the feature segmentation and the edge contour feature detection of the marathon moving video image are carried out in combination with the block area template matching method, the similarity information fusion model is used for carrying out the video information fusion awareness and the block area template matching in the process of the marathon moving video image segmentation, the fuzzy feature quantity of the moving video image of the marathon is extracted, and the machine learning method is adopted to realize the fusion awareness and the segmentation quality evaluation of the marathon moving video information. The simulation results show that the method is good in image segmentation quality and high in image recognition, and the output signal-to-noise ratio of the motion feature reconstruction of the marathons moving video is high.