A Video Classification Method Based on 3D Convolutional Neural Network

Abstract

The invention discloses a video classification method based on a three-dimensional convolutional neural network, belonging to the technical field of video processing. This method samples the video into multiple video segments at equal intervals, expands the video database, directly inputs the 3D video segment into the 3D CNN, and extracts the temporal and spatial features of the video at the same time, which improves the traditional video classification method due to manual selection of video. Limitations of feature and video modeling methods. The parallel distributed 3D CNN multi-classification model reduces the complexity of 3D CNN learning, and at the same time makes it easier for the classification system to implement distributed parallel computing. The 3D CNN-based multi-classification system can achieve a high recognition rate with only a small number of video segments, and can classify videos that do not belong to any category into new categories, avoiding classification errors for new categories.

Description

technical field [0001] The invention relates to a video classification method and belongs to the technical field of video processing. Background technique [0002] With the development of multimedia technology and Internet technology, people can now easily obtain a lot of video data from various channels, but because these massive video data are too large, how to classify these video data, so that people can more conveniently Obtaining the data you are interested in has become one of the very important and challenging research hotspots in the field of computer vision. [0003] The classification technology of video mainly includes three methods of video-based visual information, text information and audio information. As the most important information in video, visual information also contains the most video information and can best represent the video category, so it is also the most worthy. Research. The traditional video classification technology based on visual informa...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a video classification method based on a small-scale video data set and a relatively low-configuration hardware condition. The method uses data set amplification and a distributed parallel computing method of a three-dimensional convolutional neural network to classify videos in multiple ways. The problem is transformed into a binary classification problem, which not only solves the problem of insufficient video data resources, but also greatly reduces the complexity of 3D convolutional neural network learning

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