1. Grid-Based Genetic Algorithm Approach to Colour Image Segmentation Marco Gallotta Keri Woods Supervised by Audrey Mbogho

2. Image Segmentation Identifying and extracting distinct, homogeneous regions from an image Simplifies the image for further processing:  shape recognition, medical imaging, face detection

3. Image Segmentation Problem: How do we segment the following?  Each petal as a region?  Stigma as a region?  Group flowers as single region?  Segment the background?

4. Human Segmentation Segmentation by human candidates Results confirm no single solution

5. Genetic Algorithms Optimisation technique that works on large search spaces Biological evolution

6. Genetic Algorithms: Chromosome Chromosome encodes a potential solution Contains parameters The chromosome is optimised using:  mutation crossover

7. Genetic Algorithms: Fitness Fitness function evaluates an individual and assigns a numerical value Used to select fittest individuals for next iteration Crucial in producing good results

8. Grid Computing A system that coordinates resources that are not subject to centralized control Dedicated and non-dedicated resources Multiple organisations pooling their unused resources Lots of computing power

9. Grid Computing

10. Problem: Segmentation Segmentation of great importance No general method of image segmentation Wide variety of images Parameters need to be tuned to get optimal results

11. Problem: Genetic Algorithms Segmentation involves much uncertainty GA cope well with uncertainty Alter parameters to optimise segmentation results

12. Problem: Computating Requirements Image segmentation and genetic algorithms computationally intensive Combined VERY computationally expensive Solution?  Work harder  Work smarter  Get help

13. Problem: GA For The Grid Genetic algorithms easily parallelisable Grid supplies “free” computational resources

14. Problem: Research Existing Techniques Edge detection Histogram thresholding Watershed Region based techniques Clustering techniques Model based techniques Many others

15. Segmentation Method Implemented Chose to implement:  Watershed  Region Growing  Region Merging

16. Watershed Transformation Calculate a gradient magnitude image Consider this as a topographic surface Consider dropping water at each pixel and observing where the trickle ends Pixels with the same end point form a region

17. Watershed Transformation (a) Example gradient magnitude image (b) The two regions that are identified

18. Watershed Transformation: Example

19. Region Growing Start off with small regions and grow them Each iteration considers all pixels neighbouring the regions Pixel with the minimum δ is added to the region This continues until all pixels are assigned to a region

20. Region Growing The above method requires manual seeds To automate we introduce a threshold T If the minimum δ exceeds T then a new region is created Start with an arbitrary pixel as the first region and iterate as above

21. Region Growing: Example

22. Region Merging Initially each pixel a region Adjacent regions merged if criteria met Continue until no regions meet criteria

23. Merging Criterion Merge if fusion factor less than scale parameter Fusion factor: change in heterogeneity if regions merged Heterogeneity: colour, compactness, smoothness Scale parameter controls size of resulting regions

24. Region Merging: Example

25. Segmentation Results Berkeley Segmentation Dataset Watershed fastest Performance results: SegmentationTime (seconds) Region Growing 71.806 Watershed Transformation 2.106 Region Merging 58.059

26. Segmentation Results

28. All successful but different results Effect of scale parameter on region merging  Large scale parameter => large regions

29. Segmentation Results: Effect of Scale Parameter

30. Segmentation Results [Can get some results off website at http://people.cs.uct.ac.za/~mgallott/honsproj/] http://people.cs.uct.ac.za/~mgallott/honsproj/

31. Genetic Algorithm Modify parameters of region merging algorithm Scale parameter, weights of components of heterogeneity

32. GA: Fitness Function Drives evolution of chromosomes Evaluate quality of segmentation Unsupervised segmentation  No external information  Properties of image itself How much colour within each region varies Low fitness = good segmentation

33. GA: Fitness Function For each region standard deviation multiplied by area Sum all regions Add 1 Multiply by number of regions

34. Genetic Algorithm Results Inconclusive Sometimes improvement

35. Genetic Algorithm Results GA with segmentation very computationally intensive Unable to explore full potential Extremely slow Therefore grid

36. Parallel Genetic Algorithms Two common models:  master-slave (left)‏  Island model (right)‏

37. Grid Computing + Genetic Algorithms With the Grid, communication between nodes is expensive (“impossible” in a true Grid)‏ Even with communication, building a topology for the Island model is difficult All existing research has used the master-slave model

38. Grid Model Our model uses ideas from both master-slave and Island models Root node (dedicated resource) stores a super population No direct communication between sub-nodes

39. Grid Model: Results We were heavily restricted in testing and could only test with eight nodes Tests showed the communication overhead had negligible impact as fitness function increased in complexity Results were positive when testing on simple problems Unsuccessful at migrating the segmentation algorithm to the Grid

40. Conclusion Experimented with 3 segmentation algorithms Selected region merging for our genetic algorithm solution Genetic algorithm provides potential for improvement but results inconclusive Grid computing showed positive results however limited resources did not allow for thorough testing

41. Future Work: Grid As we only tested on a small Grid, we never had scalability issues Most Grids are very large and having a single root node is a bottleneck The next stage is to test out a hierarchical model

42. Future Work: GA Watershed with genetic algorithm Investigate different fitness functions Genetic programming to evolve fitness function  Train for each desired application

43. Questions ? http://people.cs.uct.ac.za/~mgallott/honsproj/