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Statistical Modeling of Texture Sketch

  • Ying Nian Wu
  • Song Chun Zhu
  • Cheng-en Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)

Abstract

Recent results on sparse coding and independent component analysis suggest that human vision first represents a visual image by a linear superposition of a relatively small number of localized, elongate, oriented image bases. With this representation, the sketch of an image consists of the locations, orientations, and elongations of the image bases, and the sketch can be visually illustrated by depicting each image base by a linelet of the same length and orientation. Built on the insight of sparse and independent component analysis, we propose a two-level generative model for textures. At the bottom-level, the texture image is represented by a linear superposition of image bases. At the top-level, a Markov model is assumed for the placement of the image bases or the sketch, and the model is characterized by a set of simple geometrical feature statistics.

Keywords

Independent component analysis Matching pursuit Minimax entropy learning Sparse coding Texture modeling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ying Nian Wu
    • 1
  • Song Chun Zhu
    • 2
  • Cheng-en Guo
    • 2
  1. 1.Dept. of StatisticsUniv. of CaliforniaLos AngelesUSA
  2. 2.Dept. of Comp. and Info. Sci.Ohio State Univ.ColumbusUSA

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