计算机科学 ›› 2016, Vol. 43 ›› Issue (7): 62-66.doi: 10.11896/j.issn.1002-137X.2016.07.010

• 2015年第二十四届全国多媒体学术会议 • 上一篇    下一篇

基于聚类的视频专题演化分析方法

谢毓湘,栾悉道,郭延明,李琛,牛晓   

  1. 国防科学技术大学信息系统与管理学院 长沙410073,长沙学院数学与计算机科学系 长沙410003,国防科学技术大学信息系统与管理学院 长沙410073,国防科学技术大学信息系统与管理学院 长沙410073,国防科学技术大学信息系统与管理学院 长沙410073
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(61571453),湖南省自然科学基金项目(14JJ3010),湖南省教育厅重点项目(15A020)资助

Video Topic Evolution Analysis Based on Clustering

XIE Yu-xiang, LUAN Xi-dao, GUO Yan-ming, LI Chen and NIU Xiao   

  • Online:2018-12-01 Published:2018-12-01

摘要: 视频专题演化分析有助于从海量的视频数据中发现有价值的模式。研究了基于聚类的视频专题演化分析方法,首先基于二部图对视频的视觉相似性进行分析;在此基础上,为增强同一专题视频之间的关联度以及不同专题视频之间的区分度,采用基于链路分析的方法对视频专题进行聚类,进而对视频专题的演化过程进行分析;最后通过实验证明了所提方法的有效性。

关键词: 视频专题,聚类,链路分析,演化进程

Abstract: Video topic evolution analysis is contributive to the discovery of valuable pattern from massive video data.In this paper,a video topic evolution analysis method based on clustering was proposed.Firstly,the paper discussed how to analyze the visual similarity between video key frames based on bipartite graphs.Secondly,we proposed a clustering method by applying link analysis to the clustering of video topics so that the relationship between the same video topics and the otherness among different video topics could be enhanced.Thirdly,we revealed the evolution procedure of video topics.Finally,some experiments were carried out to prove the effectiveness of the proposed method.

Key words: Video topics,Clustering,Link analysis,Evolution procedure

[1] Shen H T,Ooi B C,Zhou X.Towards effective indexing for very large video sequence database[C]∥Proceedings of the 2005 ACM SIGMOD international conference on Management of data.ACM,2005:730-741
[2] Bai L,Lao S,Smeaton A F,et al.Automatic summarization of rushes video using bipartite graphs[M]∥Semantic Multimedia.Springer Berlin Heidelberg,2008:3-14
[3] Wu Yu-hong,General Overview on Clustering Algorithms[J].Computer Science,2015,42(6A):491-499(in Chinese) 伍育红.聚类算法综述[J].计算机科学,2015,42(6A):491-499
[4] Wu X,Ngo C W,Hauptmann A G.Multimodal news story clustering with pairwise visual near-duplicate constraint[J].IEEE Transactions on Multimedia,2008,10(2):188-199
[5] Nallapati R,Feng A,Peng F,et al.Event threading within news topics[C]∥Proceedings of the thirteenth ACM International Conference on Information and Knowledge Management.ACM,2004:446-453
[6] Ide I,Mo H,Katayama N.Threading news video topics[C]∥Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval.ACM,2003:239-246
[7] Duygulu P,Pan J Y,Forsyth D A.Towards auto-documentary:tracking the evolution of news stories[C]∥Proceedings of the 12th Annual ACM International Conference on Multimedia.ACM,2004:820-827
[8] Ide I,Kinoshita T,Takahashi T,et al.mediaWalker:A video archive explorer based on time-series semantic structure[C]∥Proceedings of the 15th International Conference on Multimedia.ACM,2007:162-163
[9] Wu X.Threading stories and generating topic structures in news videos across different sources[C]∥Proceedings of the 13th Annual ACM International Conference on Multimedia.ACM,2005:1047-1048
[10] Wu X,Ngo C W,Li Q.Threading and autodocumenting news videos:a promising solution to rapidly browse news topics[J].Signal Processing Magazine,IEEE,2006,23(2):59-68
[11] Steinbach M,Karypis G,Kumar V.A comparison of document clustering techniques[C]∥KDD Workshop on Text Mining.2000:525-526

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