Spring Semester Summary 5/6/2011 Jacob D’Avy

1. Spring Semester Summary5/6/2011Jacob D’Avy

2. Outline • Semester tasks summary • Writing update • Software utilities • Moving forward/Summer

3. Outline • Semester tasks summary • Writing update • Software utilities • Moving forward/Summer

4. Tasks Writing • Parameter optimization review • Performance evaluation review (supervised & unsupervised) • Segmentation paper summaries Research focus • Learning state of the art of segmentation, parameter optimization, and performance evaluation. • Testing segmentation and parameter optimization methods • Potential collaborations with other IRIS students Utility development • Updating segmentation utility GUI • Platform for testing performance evaluation methods • System to run and visualize parameter search process

5. Outline • Semester tasks summary • Writing update • Software utilities • Moving forward/Summer

6. Writing http://imaging.utk.edu/research/jdavy/reports.htm

7. Outline • Semester tasks summary • Writing update • Parameter Optimization Review • Performance Evaluation Review • Software utilities • Moving forward/Summer

8. Parameter Optimization Review • A review of optimization methods that have been applied to finding parameters for segmentation. • List of methods contained in the review: * Reference list at end of presentation

9. Parameter Optimization Review • Heuristic search methods • - Generate parameters • - Segment the image • - Evaluate performance Image Generate parameters Segment Evaluate

10. Parameter Optimization Review • Heuristic search methods • - Generate parameters • - Segment the image • - Evaluate performance Image Generate parameters Segment Evaluate • Crucial evaluation feedback • Time consuming • Local minima

11. Outline • Semester tasks summary • Writing update • Parameter Optimization Review • Performance Evaluation Review • Software utilities • Moving forward/Summer

12. Unsupervised Performance Evaluation Review • A review of methods that rate the “goodness” of a segmentation without using ground truth. I have implemented F, Q, and Color saliency methods. * Reference list at end of presentation

13. Unsupervised Performance Evaluation Review • There are much less unsupervised methods than supervised. • Most evaluation measures try to fulfill the criteria: • Regions should be uniform and homogeneous. • Adjacent regions should be significantly different. • Boundaries should be simple. • Not an easy problem. Summary: R. Haralick, L. Shapiro, “Image Segmentation Techniques,” Computer Vision, Graphic, and Image Processing, vol. 29, pp. 100-132, 1985.

14. Outline • Semester tasks • Writing update • Software utilities • Segmentation Analysis GUI • Parameter Optimization platform • Moving forward/Summer

15. Utility development Segmentation Analysis GUI • Segment image for a range of parameters • Visualize segmentation results • Ability to test performance evaluation methods GUI Functionality Segmentation Evaluation New version is available on my website http://imaging.utk.edu/research/jdavy/webfiles/code/segUtil/segUtilv050411.zip

16. Outline • Semester tasks • Writing update • Software utilities • Segmentation Analysis GUI • Parameter Optimization platform • Moving forward/Summer

17. Utility development Parameter optimization testing platform • Parameter search using Tabu search • Visualization of search process D. Crevier, “Image Segmentation Algorithm Development Using Ground Truth Image Datasets,” Computer Vision and Image Understanding, vol. 112, no. 2, pp. 143-159, 2008.

18. Tabu search example • Tabu search can be used to find parameters for a segmentation method. • Tabu search uses a memory system to modify a neighborhood search window. • New parameter combinations are generated within the search window. 1. Generate parameter combinations 2. Segment Evaluation performance 3. Generate new search neighborhood F. Glover, “Tabu Search: A Tutorial,” Interfaces, vol. 20, 1990.

19. Tabu search example Input image: Segmentation method: Efficient graph based Evaluation method: Color saliency Parameter data: P. Felzenszwalb and D. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of Computer Vision, vol. 59, no. 2, 2004 . G. Heidemann, “Region saliency as a measure for colour segmentation stability,” Image and Vision Computing, vol. 26, no. 2, pp. 211-227, 2008.

20. Tabu search example Input image: Highest CS score parameters : Segmented result using these parameters:

21. Outline • Semester tasks • Writing update • Software utilities • Moving forward/Summer

22. Moving Forward Summer Find collaboration opportunities with other IRIS students Develop and test parameter optimization idea Segmentation, parameter optimization, performance evaluation papers Tyndall? *Wedding in July

23. Parameter Optimization Review References

24. Performance Evaluation Review References

25. End