[1]LIU Lu,JIA Caiyan.Web video clustering method based on an extended text model[J].CAAI Transactions on Intelligent Systems,2017,12(6):799-805.[doi:10.11992/tis.201706036]
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Web video clustering method based on an extended text model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 6 Page number: 799-805 Column: 学术论文—机器学习 Public date: 2017-12-25
Title:
Web video clustering method based on an extended text model
Author(s):
LIU Lu1; 2;  JIA Caiyan1; 2
1. Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China;
2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Keywords:
web video clustering; co-click videos; relevant inquiry word; text clustering
CLC:
TP391
DOI:
10.11992/tis.201706036
Abstract:
With the rapid rise and development of video sharing websites, there has been an explosive increase in web videos on the Internet. Effective organization and classification are necessary for the valid use of such videos. Video clustering technology has gained increasing popularity because it considers the internal cluster structure of video data, and no manual intervention is necessary. There are many video clustering algorithms in existence, such as those based on the visual similarity of key frames, text clustering of video titles, and multi-model fusion by integrating text and visual features. The video clustering method based on the text clustering of titles has become a widely used method in business because of its simplicity and efficiency. However, it performs poorly due to the semantic sparsity of short titles. Therefore, this paper proposes a video clustering method with related text fusion from multiple sources on social media platforms to overcome the semantic sparsity of short text. The experimental results on different text clustering algorithms demonstrate the effectiveness of this method.
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