Affiliation:
1. Physical Education Institute, Xinxiang Medical University, Xinxiang 453003, Henan, China
Abstract
With the rapid development of information technology, digital content shows an explosive growth trend. Sports video classification is of great significance for digital content archiving in the server. Therefore, the accurate classification of sports video categories is realized by using deep neural network algorithm (DNN), convolutional neural network (CNN), and transfer learning. Block brightness comparison coding (BICC) and block color histogram are proposed, which reflect the brightness relationship between different regions in video and the color information in the region. The maximum mean difference (MMD) algorithm is adopted to achieve the purpose of transfer learning. On the basis of obtaining the features of sports video images, the sports video image classification method based on deep learning coding model is adopted to realize sports video classification. The results show that, for different types of sports videos, the overall classification effect of this method is obviously better than other current sports video classification methods, which greatly improves the classification effect of sports videos.
Subject
General Mathematics,General Medicine,General Neuroscience,General Computer Science
Reference27 articles.
1. Articulatory Modeling for Pronunciation Error Detection without Non-Native Training Data Based on DNN Transfer Learning
2. A Novel Framework for Trash Classification Using Deep Transfer Learning
3. A combined recommendation algorithm of MF and DNN with transfer learning;Y. Shen;Journal of Air Force Early Warning Academy,2019
4. Image classification technology based on convolutional neural network;H. Tong;Science and Technology Vision,2017
5. Aircraft target recognition based on transfer learning of convolutional neural network;Y. Yang;Modern Radar,2019
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