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Super-Patches

Super-Patches. Image Tessellation with Arbitrarily Shaped Patches. Eddie K. H. Ng , University of Toronto. Joint work with Brendan Frey. Motivations. Patch-based models have been successfully applied to many different areas of computer vision.

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Super-Patches

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  1. Super-Patches Image Tessellation with Arbitrarily Shaped Patches Eddie K. H. Ng, University of Toronto Joint work with Brendan Frey.

  2. Motivations • Patch-based models have been successfully applied to many different areas of computer vision. • Patch size / shape selection remains a skillful labor and confined to simple geometric shapes. • Causes problems in part-based object recognition. CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

  3. Goals • Learn a set of coherent patches of arbitrary shapes and sizes. • Use the resulting patches to enhance performance of various tasks in computer vision. CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

  4. Inspiration • Super-pixels [Ren & Malik, 2003] • Create a local, coherent entity while maintaining overall structure • Reduce computational complexity. X. Ren and J. Malik. Learning a classification model for segmentation. In Proc. 9th Int. Conf. Computer Vision, volume 1, pages 10-17, 2003. * Image taken from http://www.cs.sfu.ca/~mori/research/superpixels/. CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

  5. Basic Concepts [1] • The shape and size of each patch is specified through a kernel function (e.g. Gaussian (m, s)). • The set of kernel functions compete for the ownership each pixel. Initial Conditions Patches after Competition Input Image Input Image CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

  6. Basic Concepts [2] • The tessellation is then a map showing the respective ownerships. • Free parameter - number of patches * Patches after Competition Tessellated Image Input Image CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

  7. Basic Concepts [4] • But how does the patches compete with each other? • Each patch is mapped to a region in the Epitome. Prior knowledge of Image Classes (epitome) Tessellated Image Input Image CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

  8. Basic Concepts [5] • Cost Function: • Optimize cost function using conjugate gradient CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

  9. Run Demo

  10. Other Explored Paths • Variations on the cost function: • Garbage Model • Isotropic Patches • Shape Regularization • Grow-and-Merge • Different formulation of the problem • Other kernel functions • Other learning algorithms CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

  11. References • Epitome • N. Jojic, B. J. Frey, A. Kannan, Epitomic analysis of appearance and shape , ICCV 2003. • Low-level vision • W.T. Freeman, E.C. Pasztor, O. T. Carmichael, Learning Low-level vision, International Journal of Computer Vision 40(1), 25-47, 2000 • Super-pixels • X. Ren and J. Malik. Learning a classification model for segmentation. In Proc. 9th Int. Conf. Computer Vision, volume 1, pages 10-17, 2003. • Web resources • www.psi.utoronto.ca • http://research.microsoft.com/~jojic/epitome.htm CIAR Summer School on Learning and Vision Aug 15 to 19, 2006

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