Multi-mode fusion video emotion identification method based on kernel-based over-limit learning machine

Abstract

The invention relates to a multi-mode fusion video emotion identification method based on a kernel-based over-limit learning machine. Feature extraction and feature selection are carried out on image information and audio information of a video so as to obtain a video feature; pretreatment, feature extraction, and feature selection are carried out on a collected multi-channel electroencephalogram signal to obtain an electroencephalogram feature; a multi-mode fusion video emotion identification model based on a kernel-based over-limit learning machine is established; the video feature and the electroencephalogram feature are inputted into the multi-mode fusion video emotion identification model based on a kernel-based over-limit learning machine to carry out video emotion identification, thereby obtaining a final classification accuracy rate. According to the invention, with the multi-mode fusion video emotion identification model based on a kernel-based over-limit learning machine, the operation becomes simple and the identification speed becomes fast; and the classification accuracy rate of three kinds of video emotion data is high. The video content description can be realized completely by using the video and electroencephalogram data; and compared with the video emotion identification by using the single mode, the two-mode-data-based video emotion identification enables the classification accuracy rate to be improved.

Description

technical field [0001] The invention relates to a pattern recognition method, in particular to a video emotion recognition method. Background technique [0002] With the high-speed development of multimedia technology, various digital videos have emerged in a short period of time. However, some videos contain content that is not suitable for minors, such as videos with violent and sexual content. For the healthy growth of minors, it is very necessary to establish a good network environment. Accurately identifying the emotional content of different videos is the basis for establishing a good network environment. How to accurately identify the emotional content of different videos is an important and challenging topic for many researchers. [0003] Compared with unimodality, multimodality can more comprehensively describe the video content, and thus can more accurately identify the emotion contained in the video. Therefore, video emotion recognition using multimodal fusion...

AI Technical Summary

Problems solved by technology

This method has two disadvantages: (1) relying solely on video information to judge the emotional content of the video without taking into account people's feelings after watching the video, it is easy to cause information deviation; (2) only relying on the information of the video itself For video emotion recognition, the recognition accuracy is low

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