Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Similar presentations


Presentation on theme: "Mean-Field Theory and Its Applications In Computer Vision4 1."— Presentation transcript:

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

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

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

4 Marginal update 4 General form of meanfield update Expectation of the cost given variable v i takes a label

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

6 Marginal Update in Meanfield 6 Some possible states Total number of possible states: 3 6 labels

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

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

9 Marginal Update in Meanfield 9 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 10 Expectation calculation is quite efficient

11 Pattern based cost 11 Segment takes one of the forms

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

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

14 P N Potts based patterns 14 P N Potts based patterns Potts patterns

15 Potts cost 15 Potts cost Potts patterns

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

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

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

19 Expectation update 19 Expectation update Potts patterns

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

21 PascalVOC-10 dataset 21 Inclusion of PN potts term: AlgorithmTime (s)OverallAv. RecallAv. I/U AHCRF+Cooc Dense CRF Dense + PN Potts 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


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

Similar presentations


Ads by Google