计算机工程 ›› 2011, Vol. 37 ›› Issue (18): 281-283.doi: 10.3969/j.issn.1000-3428.2011.18.094

• 开发研究与设计技术 • 上一篇    下一篇

基于关联图谱的视频片段聚类

吴永龙 1,符茂胜 1,2,罗 斌 1   

  1. (1. 安徽大学计算智能与信号处理教育部重点实验室,合肥 230039;2. 皖西学院计算机科学与技术系,安徽 六安 237012)
  • 收稿日期:2011-01-26 出版日期:2011-09-20 发布日期:2011-09-20
  • 作者简介:吴永龙(1986-),男,硕士研究生,主研方向:模式识别,视频分析;符茂胜,副教授、博士研究生;罗 斌,教授、博士生导师
  • 基金项目:

    国家自然科学基金资助项目(60772122);高校博士点基金资助项目(20070357001);安徽省教育厅自然科学重点科研计划基金资助项目(KJ2010A326)

Video Clips Clustering Based on Spectral of Correlative Graph

WU Yong-long 1, FU Mao-sheng 1,2, LUO Bin 1   

  1. (1. Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei 230039, China; 2. Department of Computer Science and Technology, West Anhui University, Lu’an 237012, China)
  • Received:2011-01-26 Online:2011-09-20 Published:2011-09-20

摘要: 提出一种基于关联图谱的视频片段聚类方法。构造视频片段的关联图并将其转换成邻接矩阵,提取邻接矩阵的主分量特征值、模间邻接矩阵和模间距离后,将三者分别嵌入主成分分析和独立成分分析模式空间中,利用k-means进行聚类分析。实验结果表明,该方法能有效区分不同类型的视频片段。

关键词: 关联图谱, 主成分分析, 独立成分分析, 谱特征, 模式空间, k-means聚类

Abstract: This paper proposes video clips clustering method based on spectral of correlative graph. It constructs the correlative graph of the video clips, changes it to an adjacency matrix, and extracts the spectral characteristics of adjacency matrix which includes the leading eigenvalues, inter-mode adjacency matrices and inter-mode edge-distance. These vectors are embedded into the pattern space by using Principal Component Analysis(PCA) and Independent Component Analysis(ICA), and k-means method is used for clustering analysis. Experimental results show that the method is effective in distinguishing different types of video clips.

Key words: spectral of correlative graph Principal Component Analysis(PCA), Independent Component Analysis(ICA), spectral characteristic, pattern space, k-means clustering

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