• DocumentCode
    498374
  • Title

    Rough Sets Based Video Mining Preprocessing Algorithm in Compressed Domain

  • Author

    Xiang-wei, Li ; Ming-xin, Zhang ; Ya-ling, Zhu ; Xing-du, Li ; Ting-bing, Ma

  • Author_Institution
    Dept. of Comput. Eng., Lanzhou Polytech. Coll., Lanzhou, China
  • Volume
    2
  • fYear
    2009
  • fDate
    19-21 May 2009
  • Firstpage
    470
  • Lastpage
    473
  • Abstract
    A critical and fundamental task in video mining is data preprocessing, in this paper, aimed to overcome limitations of redundant data for video mining, the paper propose a video mining preprocessing algorithm based on Rough Sets. Firstly, the representative data of video sequences is extracted in compressed domain. Secondly, the Information System Table is constructed by extracted representative data. Finally, the Core of Information System Table is achieved by making use of the attributes reduction theory of RS. As our experimental results indicate, the algorithm can get effective and scientific data to complete video mining such as key frame extraction and shot segmentation and other operations. Compared to existing techniques, our proposed algorithm enjoys following advantages. (1) only a subset of frames need to be considered during video mining. (2) The limitations of requirements for a huge amount of memory and CPU resource are overcome.
  • Keywords
    data mining; data reduction; rough set theory; video coding; attributes reduction theory; compressed domain; data preprocessing; information system table; key frame extraction; redundant data; rough sets; shot segmentation; video mining; video sequences; Data mining; Data preprocessing; Educational institutions; Information systems; Intelligent systems; Internet; Mathematics; Probability; Rough sets; Video compression; Rough Sets; compressed domain; data mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-0-7695-3571-5
  • Type

    conf

  • DOI
    10.1109/GCIS.2009.150
  • Filename
    5209378