Abstract
There are many different types of sports training films, and categorizing them can be difficult. As a result, this research introduces an autonomous video content classification system that makes managing large amounts of video data easier. This research provides a video feature extraction approach using a support vector machine (SVM) video classification algorithm and a mix of video and audio dual-mode characteristics. It automates the classification of cartoons, ads, music, news, and sports videos, as well as the detection of terrorist and violent moments in films. To begin, a new feature expression scheme, the MPEG-7 visual descriptor subcombination, is proposed based on an analysis of the existing video classification algorithms, with the goal of addressing the problems in these algorithms. This is accomplished by analyzing the visual differences of the five video classification algorithms. The model was able to extract 9 descriptors from the four characteristics of color, texture, shape, and motion, resulting in a new overall visual feature with good results. The results suggest that the algorithm optimizes video segmentation by highlighting disparities in feature selection between different categories of films. Second, the support vector machine’s multivideo classification performance is improved by the enhanced secondary prediction method. Finally, a comparison experiment with current related similar algorithms was conducted. The suggested method outperformed the competition in the accuracy of video classification in five different types of videos, as well as in the recognition of terrorist and violent incidents.
Highlights
Introduction e rapid expansion of Internet and 4G network bandwidth has resulted in the explosive proliferation of various multimedia video streams in recent years
Erefore, this paper proposes an automatic sports video frequency classification method based on support vector machine. e experimental results show that the proposed method can realize classification quickly and obtain high classification accuracy
This research provides a support vector machine (SVM)-based automatic sports video categorization approach that combines the visual bag of words model with principal component analysis technology from the previous literature
Summary
An Automatic Classification Method of Sports Teaching Video Using Support Vector Machine. Is research provides a video feature extraction approach using a support vector machine (SVM) video classification algorithm and a mix of video and audio dual-mode characteristics. It automates the classification of cartoons, ads, music, news, and sports videos, as well as the detection of terrorist and violent moments in films. Erefore, this paper proposes an automatic sports video frequency classification method based on support vector machine. This research provides a support vector machine (SVM)-based automatic sports video categorization approach that combines the visual bag of words model with principal component analysis technology from the previous literature. Domain-specific visual features are related to specific applications, such as facial features, fingerprint features, and handwriting, whereas universal visual features are used to characterize the common features of all movies, such as color and texture. e object studied in this paper is the classification of video types, rather than the classification of specific objects, so the general visual features are mainly used. e following is a detailed introduction [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
