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Nonparametric Bayesian Texture Learning and Synthesis

This work explores nonparametric Bayesian techniques for texture learning and synthesis, leveraging approaches such as image quilting and hierarchical Dirichlet processes combined with 2D-HMM (Hidden Markov Models) for texture analysis. The methodology is demonstrated through various applications, including aerial image analysis, automatic terrain categorization, fast satellite map synthesis, and random map generation. Results showcase the model’s capability to handle regular, stochastic, and mixture textures, providing valuable insights for image processing and analysis in remote sensing.

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Nonparametric Bayesian Texture Learning and Synthesis

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  1. Nonparametric Bayesian Texture Learning and Synthesis Leo Zhuand Bill Freeman Joint work with Chen and Torralba

  2. Outline • Task: Texture Learning and Synthesis • Approach: Nonparametric Bayesian Learning and Image Quilting • Texture Model: Hierarchical Dirichlet Process + 2D-HMM

  3. Texture Analysis and Synthesis Texture Analysis Input Texture Texture Synthesis

  4. Flow Chart

  5. Image Features: Filter Responses

  6. Graphical Model: HDP + 2D-HMM

  7. Learning

  8. Result I: Regular Texture

  9. Result II: Stochastic Texture

  10. Result III: Mixture Texture (natural)

  11. Applications: Aerial Image Analysis • Automatic terrain categorization and annotation • Fast satellite map synthesis • Satellite map compression • Fake/random map generation

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