• DocumentCode
    442138
  • Title

    An incremental Bayesian approch to sketch recognition [approach read approach]

  • Author

    Liao, Shi-Zhong ; Wang, Xiao-Jun ; Lu, Jin-Liang

  • Author_Institution
    Sch. of Electron. & Inf. Eng., Tianjin Univ., China
  • Volume
    7
  • fYear
    2005
  • fDate
    18-21 Aug. 2005
  • Firstpage
    4549
  • Abstract
    Sketch recognition is an essential process for sketch understanding. The free drawing style of sketching makes it difficult to build a robust sketch recognition system that can support the imprecision and high variability present in sketch. This paper addresses these problems inherent in sketch recognition in the framework of Bayesian network which can readily represent uncertainty in the recognition process and make inference based on partial evidence. To further improve recognition accuracy, context information is incorporated in the recognition process rather than identifying sketches in isolation. The new framework proposed in this paper offers user more sketching freedom by automatically grouping strokes and recognizing editing gestures as well as over-traced strokes.
  • Keywords
    belief networks; image recognition; learning (artificial intelligence); uncertainty handling; Bayesian network; editing gestures; free drawing; incremental Bayesian approach; over-traced strokes; partial evidence; sketch identification; sketch recognition; sketch understanding; stroke grouping; structure matching; uncertainty representation; Aggregates; Artificial intelligence; Bayesian methods; Character recognition; Engineering drawings; Machine learning algorithms; Optical character recognition software; Robustness; Support vector machines; Uncertainty; Bayesian network; over-traced stroke; sketch understanding; strokes grouping; structure match;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
  • Conference_Location
    Guangzhou, China
  • Print_ISBN
    0-7803-9091-1
  • Type

    conf

  • DOI
    10.1109/ICMLC.2005.1527740
  • Filename
    1527740