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Mean-Field Theory and Its Applications In Computer Vision4

Mean-Field Theory and Its Applications In Computer Vision4. Motivation. Helps in incorporating region/segment consistency in the model. Pairwise CRF. Higher order CRF. Motivation. Higher order terms can help in incorporating detectors into our model. Without detector. With detector. Image.

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Mean-Field Theory and Its Applications In Computer Vision4

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  1. Mean-Field Theory and Its Applications In Computer Vision4

  2. Motivation Helps in incorporating region/segment consistency in the model Pairwise CRF Higher order CRF

  3. Motivation Higher order terms can help in incorporating detectors into our model Without detector With detector Image

  4. Marginal update General form of meanfield update Expectation of the cost given variable vi takes a label

  5. Marginal Update General form of meanfield update Expectation of the clique given variable vi takes a label • Summation over the possible states of the clique

  6. Marginal Update in Meanfield Some possible states labels Total number of possible states: 36

  7. Marginal Update in Meanfield Exponential # of possible states for clique of size |c| and labels L: |L|C Expectation evaluation (summation) becomes infeasible

  8. Marginal Update in Meanfield • Use restricted form of cost • Pattern based potential

  9. Marginal Update in Meanfield Restrict the number of states to certain number of patterns Simple patterns Segment takes a label from label set of 4 patterns Or none

  10. Marginal Update in Meanfield Expectation calculation is quite efficient

  11. Pattern based cost Segment takes one of the forms

  12. Pattern based cost Segment does not take one of the forms

  13. Pattern based cost • Simple patterns Simple patterns • Pattern based higher order terms

  14. PN Potts based patterns • PN Potts based patterns Potts patterns

  15. Potts cost • Potts cost Potts patterns

  16. Marginal Update in Meanfield General form of meanfield update Expectation of the cost given variable vi takes a label

  17. Expectation update Probability of segment taking that label Potts patterns

  18. Expectation update Probability of segment not taking that label Potts patterns

  19. Expectation update Expectation update Potts patterns

  20. Complexity • Expectation Updation: • Time complexity • O(NL) • Preserves the complexity of original filter based method

  21. PascalVOC-10 dataset • Inclusion of PN potts term: • Slight improvement in I/U score compared to more complex model which includes Pn Potts + cooccurrence terms • Almost 8-9 times faster than the alpha-expansion based method

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