1. Segmentation Through Optimization Pyry Matikainen

2. “He who fights with monsters should look to it that he himself does not become a monster.” -Friedrich Nietzsche, Beyond Good and Evil

3. Formulate Problem Force problem into favorite algorithm “Refine” Gradient ascent via parameter tweaking Publish Retroactively justify decisions

4. What is wrong with this? Difficult to use Difficult to extend Difficult to study Formulate Problem Force problem into favorite algorithm “Refine” Gradient ascent via parameter tweaking Publish Retroactively justify decisions

5. Z. Tu and S. C. Zhu (2002) to the rescue! and also Ren and Malik (2003)…

6. Z. Tu and S. C. Zhu. Image Segmentation by Data-Driven Markov Chain Monte Carlo. PAMI, vol.24, no.5, pp. 657-673, May, 2002: The DDMCMC paradigm combines and generalizes these [all other] segmentation methods in a principled way.

7. Optimizer

11. “What is a good segment?” Ren and Malik (2003)

13. How do we model a segment? Raw pixel values Contours Texture

15. G(x) h(x) h(f(x)) G(b(x) - x) x2

16. (gaussian)(histogram)(gabor)(Bezier)

18. Number of regions Region perimeter length (smoothness) Region area Region appearance model complexity Notably absent: the data

20. Superpixels (normalized cuts) Oriented energy Brightness Texture (textons)

21. Classifier * G(W|I)

25. Optimizer

27. MCMC is a technique for sampling from distributions.

29. Number of regions Region Region? ?? ?

30. Merge Split Boundary competition Switching image models Model adaptation Ren and Malik The ‘data driven’ part revealed!

34. Data driven = do some clustering to make the MCMC faster.

35. Optimizer

36. Tu & Zhu

38. Ren & Malik

41. Tu & ZhuRen & Malik New paradigm? Combines and generalizes other techniques? Principled? Good results? 1/2 0 00 11/3

42. Optimizer

43. (gaussian) (mixture of gaussians) (3x Bezier spline)

44. (gaussian) (g2) (g3) (g4) (g1) (histogram) (gabor filter) (Bezier spline)

45. Number of regions Pixels in region Region appearance model Region appearance model parameters

46. MCMC

47. Xiaofeng Ren and Jitendra Malik. Learning a Classification Model for Segmentation. ICCV 2003.

48. Boundary between i and j

49. Tu and Zhu 2002 Sampling P(W|I) Generative models Pixels Ren and Malik 2003 Maximizing G(W|I) Discriminative models Superpixels Classification certainty