Horror video identification method based on discriminant instance selection and multi-instance learning

Abstract

The invention discloses a horror video identification method based on discriminant instance selection and multi-instance learning. The method comprises the steps of extracting the video shot of each video in a training video set and selecting an emotional representative frame and an emotional mutation frame for each video shot to show the shot, extracting the audio and video characteristics of the shots as video instances to form a video instance set, selecting a discriminant video instance from the video instance set, calculating the similarity distances between the video instances and the discriminant video instance in the training video set to obtain a characteristic vector set, carrying out sparse reconstruction on the characteristic vector of the video to be identified and the characteristic vector set corresponding to the training video set, and identifying the class of the video according to reconstruction errors. The novel horror video identification method based on the discriminant instance selection and the multi-instance learning is applied to horror film scene identification, is of great academic significance and social significance and has wide application prospects.

Description

Technical field [0001] The present invention relates to the field of pattern recognition and computer network content security, in particular to a method for identifying scary videos based on discriminative example selection and multi-instance learning. Background technique [0002] With the rapid development of Internet technology and applications, people's understanding and use of the Internet have become more and more in-depth. At the same time, the Internet has also brought a lot of convenience to people's lives and even changed people's lifestyles. On the basis of the rapid development of the Internet, the dissemination of harmful information such as pornography, violence, and terror has also become easier. Psychological and physiological studies have shown that terrorist information on the Internet is no less harmful to the physical and mental health of young people than pornographic information. Excessive horror information may cause people to be in extreme anxiety and fe...

AI Technical Summary

Problems solved by technology

The difficulty in the multi-instance learning problem is that the exact labels of the examples in the bag cannot be known, and some negative examples in the positive bag bring redundancy and interference information to the bag itself.

Similarly, through the observation of a large number of horror videos, it is found that non-horror video frames in horror videos bring a lot of redundant information and noise to video recognition

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