Hierarchical image motion segmentation using swendsen wang cuts
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Hierarchical Image-Motion Segmentation using Swendsen-Wang Cuts. Adrian Barbu Siemens Corporate Research Princeton, NJ. Acknowledgements: S.C. Zhu , Y.N. Wu, A.L. Yuille et al. Talk Outline. The Swendsen-Wang Cuts algorithm The original Swendsen-Wang algorithm

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Hierarchical Image-Motion Segmentation using Swendsen-Wang Cuts

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Hierarchical image motion segmentation using swendsen wang cuts

Hierarchical Image-Motion Segmentation using Swendsen-Wang Cuts

Adrian Barbu

Siemens Corporate Research

Princeton, NJ

Acknowledgements: S.C. Zhu , Y.N. Wu, A.L. Yuille et al.


Talk outline

Talk Outline

  • The Swendsen-Wang Cuts algorithm

    • The original Swendsen-Wang algorithm

    • Generalization to arbitrary probabilities

  • Multi-Grid and Multi-Level Swendsen-Wang Cuts

  • Application: Hierarchical Image-Motion Segmentation

  • Conclusions and future work


Swendsen wang for ising potts models

Swendsen-Wang for Ising / Potts Models

Swedsen-Wang (1987) is an extremely smart idea that flips a patch at a time.

Each edge in the lattice e=<s,t> is associated a probability q=e-b.

1. If s and t have different labels at the current state, e is turned off.

If s and t have the same label, e is turned off with probability q.

Thus each object is broken into a number of connected components (subgraphs).

2. One or many components are chosen at random.

3. The collective label is changed randomly to any of the labels.


The swendsen wang algorithm

The Swendsen-Wang Algorithm

Pros

  • Computationally efficient in sampling the Ising/Potts models

    Cons:

  • Limited to Ising / Potts models and factorized distributions

  • Not informed by data, slows down in the presence of an external field (data term)

Swendsen Wang Cuts

  • Generalizes Swendsen-Wang to arbitrary posterior probabilities

  • Improves the clustering step by using the image data


Sw cuts the acceptance probability

SW Cuts: the Acceptance Probability

Theorem (Barbu,Zhu ‘03). The acceptance probability for the Swendsen-Wang Cuts algorithm is

Theorem (Metropolis-Hastings) For any proposal probability q(AB) and probability p(A), if the Markov chain moves by taking samples from q(A  B) which are accepted with probability

then the Markov chain is reversible with respect to p and has stationary distribution p.


The swendsen wang cuts algorithm

1. Initialize a graph partition 2. Repeat, for current state A= π

3. Repeat for each subgraph Gl=<Vl, El>, l=1,2,...,n in A 4. For e El turn e=“on” with probability qe. 5. Partition Gl into nl connected components: gli=<Vli, Eli>, i=1,...,nl 6. Collect all the connected components in CP={Vli: l=1,...,n, i=1,...,nl}.

8. Propose to reassign V0 to a subgraph Gl’, l' follows a probability q(l'|V0,A)

State A

State B

CP

The Swendsen-Wang Cuts Algorithm

Swendsen-Wang Cuts: SWCInput: Go=<V, Eo>, discriminative probabilities qe, e Eo, and generative posterior probability p(W|I).Output: Samples W~p(W|I).

The initial graphGo

7. Select a connected component V0CP at random

9. Accept the move with probability α(AB).


Advantages of the sw cuts algorithm

Advantages of the SW Cuts Algorithm

  • Our algorithm bridges the gap between the specialized and generic algorithms:

    • Generally applicable – allows usage of complex models beyond the scope of the specialized algorithms

    • Computationally efficient – performance comparable with the specialized algorithms

    • Reversible and ergodic – theoretically guaranteed to eventually find the global optimum


Hierarchical image motion segmentation

Hierarchical Image-Motion Segmentation

Three-level representation:

  • Level 2: Intensity regions are grouped into

    moving objects Oiwith motion parametersqi

  • Level 1: Atomic regions are grouped into

    intensity regions Rijof coherent motion

    with intensity models Hij

  • Level 0: Pixels are grouped into atomic regions

    rijkof relatively constant motion and intensity

    • motion parameters (uijk,vijk)

    • intensity histogram hijk


Multi grid swc

Multi-Grid SWC

Select an attention window ½ G.

Cluster the vertices within  and select a connected component R

Swap the label of R

Accept the swap with probability , using as boundary condition.


Multi level swc

Multi-Level SWC

  • Select a level s, usually in an increasing order.

  • Cluster the vertices in G(s) and select a connected component R

  • Swap the label of R

  • Accept the swap with probability, using the lower levels, denoted by X(<s), as boundary conditions.


Hierarchical image motion segmentation1

Hierarchical Image-Motion Segmentation

Modeling occlusion

  • Accreted (disoccluded) pixels

  • Motion pixels

Bayesian formulation

Accreted pixels

Motion pixels explained by motion

Intensity segmentation factor with generative and histogram models.


Hierarchical image motion segmentation2

  • Main motion for each object

  • Boundary length

  • Number of labels

Hierarchical Image-Motion Segmentation

The prior has factors for

  • Smoothness of motion


Designing the edge weights

Level 1:

Histogram Hi

Histogram Hj

Level 2:

Motion histogram Mi

Motion histogram Mj

Designing the Edge Weights

  • Level 0:

    • Pixel similarity

    • Common motion


Experiments

Experiments

Input sequence

Image Segmentation

Motion Segmentation

Input sequence

Image Segmentation

Motion Segmentation


Experiments1

Experiments

Input sequence

Image Segmentation

Motion Segmentation

Input sequence

Image Segmentation

Motion Segmentation


Conclusion

Conclusion

Two extensions:

  • Swendsen-Wang Cuts

    • Samples arbitrary probabilities on Graph Partitions

    • Efficient by using data-driven techniques

    • Hundreds of times faster than Gibbs sampler

  • Marginal Space Learning

    • Constrain search by learning in Marginal Spaces

    • Six orders of magnitude speedup with great accuracy

    • Robust, complex statistical model by supervised learning


Future work

Future Work

  • Algorithm Boosting

    • Any algorithm has a success rate and an error rate

    • Can combine algorithms into a more robust algorithm by supervised learning

    • Proof of concept for Image Registration

  • Hierarchical Computing

    • Efficient representation of Top-Down and Bottom-Up communication using specialized dictionaries

    • Robust integration of multiple MSL paths by Algorithm Boosting

  • Applications to medical imaging

    • 3D curve localization and tracking

    • Brain segmentation

    • Lymph node detection


References

References

  • A. Barbu, S.C. Zhu. Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities, IEEE Trans. PAMI, August 2005.http://www.stat.ucla.edu/~abarbu/Research/partition-pami.pdf

  • A. Barbu, S.C. Zhu. Generalizing Swendsen-Wang for Image Analysis. To appear in J. Comp. Graph. Stat. http://www.stat.ucla.edu/~abarbu/Research/jcgs.pdf

    Thank You!


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