• DocumentCode
    3103991
  • Title

    Using a hierarchical approach to avoid over-fitting in early vision

  • Author

    Howard, Cheryl G. ; Bock, Peter

  • Author_Institution
    Res. Inst. for Appl. Knowledge Process., Ulm, Germany
  • Volume
    1
  • fYear
    1994
  • fDate
    9-13 Oct 1994
  • Firstpage
    826
  • Abstract
    The ALISA system is an adaptive learning image analysis system whose hierarchical design allows learning at two levels: texture and geometry. Earlier experiments using only the texture level were repeated using the combination of the texture and geometry modules to demonstrate the advantages of learning without resorting to inventing application-specific features which over-fit the domain. The two-level approach achieves quantitative results comparable with the single-level approach, but requires far fewer training examples and uses simple general-purpose features. The hierarchical approach also generates output class maps that are isomorphic with the original image and preserve important structures, and which therefore may be used for further processing
  • Keywords
    computer vision; ALISA system; adaptive learning image analysis system; early vision; geometry module; hierarchical system; texture module; Adaptive systems; Geometry; Image segmentation; Image texture analysis; Learning systems; Noise robustness; Pixel; Shape; Signal generators; Statistical learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 1994. Vol. 1 - Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on
  • Conference_Location
    Jerusalem
  • Print_ISBN
    0-8186-6265-4
  • Type

    conf

  • DOI
    10.1109/ICPR.1994.576458
  • Filename
    576458