Image Segmentation based on multicue fusion

1. Image Segmentation based on multicue fusion XiaoweiGeng 2007.09.24

2. Content • Introduction about image segmentation • image segmentation priciples • Main Process • Related Results

3. Main Principles • Conditional Random Fields Model • Mean Shift For color likelihood estimation • Gabor Filters For texture likelihood estimation • Energy Optimized by Graph Cuts

4. Conditional Random Fields • Definition (1) G=(V,E) is a graph (2) Y is indexed by the vertices of G . (3) the random variables obey the Markov property with respect to the graph, when conditioned on X : (X,Y) is a conditional random field. where means that w and v are neighbors in G.

5. Conditional Random Fields • Characteristics where x is data information,y is label information,and y|s is the set of components of y associated with the vertices in subgraph S.

6. Kernel Density Estimation (1) Kernel density estimator (2) two methods of H chosen in practice

7. Mean Shift • A method to find modes of function

9. Gabor Filters • Biological enlightenment • The Gabor Filters characteristics

10. Gabor Filters

11. Image Segmentation based on CRF • Construct Energy Function

12. Energy Function • Our Energy Function As the similar presentation of MRF,we still use the classification to explain every term.

13. Color Likelihood Term • Similar to Bayesian explanation,we define Here ,xi is the mean value of background or foreground sample

14. Texture Likelihood Term • First , get the feature image by Gabor filters • second, use mean shift to filter the interesting regions to get the mean feature vector • Third ,use kernel density estimation to get the texture likelihood term

15. Gabor Filter banks

16. Gabor Filters

17. Smooth Term • Smooth Term in order to make the results more smooth,we define the color smooth and texture smooth Terms as:

18. Energy Optimize • Graph Cuts

19. Some Results

20. Some Results

21. Some Results

22. Some Results

23. Some Results

24. Some Results