Label Distribution Learning for Video Summarization

被引：0

作者：

Liu Y. ^{[1
]}

Tang S. ^{[1
]}

Gao Y. ^{[1
]}

Li Z. ^{[1
]}

Li H. ^{[2
,3
]}

机构：

[1] College of Computer & Communication Engineering, China University of Petroleum, Qingdao

[2] Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing

[3] University of Chinese Academy of Sciences, Beijing

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 01期

关键词：

Key frame; Label distribution learning model; Multi-label learning; Video summarization;

D O I：

10.3724/SP.J.1089.2019.17281

中图分类号：

学科分类号：

摘要：

There is a problem of complicated model training in the supervised video digest algorithm. To solve this problem, a new video summary algorithm based on label distribution learning (LDL) is proposed. This algorithm uses non-parametric supervised learning to generate summarization. The main idea is to transfer summary structures from the annotated video to the same type of test video by label passing. Firstly, the convolutional neural network features and color features of the video are extracted. A feature matrix is obtained by combining these two features and reducing the dimension. It is then entered into the LDL model along with the label distribution of the training samples. Finally, the key frames are selected according to the label distribution of the model output, and they are composed into a video summary. By comparing the experiments with other algorithms on the benchmarks, it shows that the summaries generated by this algorithm are highly consistent with the hu-man-created abstract, which is obviously superior to other methods. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：104 / 110

页数：6

共 27 条

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