Using Pixel Pairing for Segmentation Parameter Selection

1. Using pixel pairing for segmentation parameter selection10/7/2010 Jacob D’Avy

2. Outline • Introduction to segmentation parameter optimization • Related work • Pixel pairing • Results

3. Parameter Optimization Motivation: • Many segmentation methods require adjusting parameters for different images.(widespread issue) • Most parameters are currently selected manually. • Any amount of guidance in parameter selection would help a user to get better segmentation results more quickly.

4. Related work Parameter optimization methods that have been used: • Semi-exhaustive search [1] • Vary parameters to produce a set of segmentation results • Evaluate each result and keep the highest score • Direct search • Move through the parameter space in an efficient way • Examples: • Tabu search[2] • Genetic algorithm[3] • Hill climbing[4] 1. M. Singh, S. Singh, and D. Partridge, “Parameter optimization for image segmentation algorithms: A systematic approach,” in Lecture Notes in Computer Science, 2005. 2. D. Crevier, “Image Segmentation Algorithm Development Using Ground Truth Image Datasets,” Computer Vision and Image Understanding, vol. 112, no. 2, pp. 143-159, 2008. 3. R. Q. Feitosa, G. A. Costa, T. B. Cazes, and B. Feijo, “A genetic approach for the automatic adaptation of segmentation parameters,” International Conference on Object-based Image Analysis, 2006. 4. J. Min, M. Powell, and K. Bowyer, “Automated Performance Evaluation of Range Image Segmentation Algorithms,” IEEE Transactions On Systems,Man, And Cybernetics, vol. 34, no. 1, 2004.

5. Related work • Finding optimal parameters for edge detection methods Process: • Find a ground truth estimate • Rank all segmentations by comparing them to the estimated ground truth and keep the parameters that produce the highest score. Y. Yitzhaky and E. Peli, “A method for objective edge detection evaluation and detector parameter selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8, 2003.

6. Outline • Introduction to segmentation parameter optimization • Related work • Pixel pairing • Results

7. Pixel pairing • The goal is to automatically find the best result from a set of • segmentations produced by varying parameters. • The approach is based on the idea that each segmentation contains • some useful information about the ideal pixel grouping. • Each segmentation is scored based on its correspondence to • the pixel pairing of all the other segmentations. • The parameter values that produce segmentations with higher • scores are considered better. input seg1 seg2 seg3 seg4

8. Pixel pairing method • Pixel pairs are made between neighboring pixels. • The value of a pixel pair is the number of times that the pixels are assigned to the same region in the segmentation images. Definitions Process Produce a set of segmentation results, , by varying the parameter values. Calculate the pixel pair values, . For each segmentation in , find the pair correspondence score(PCS). The parameters that produced segmentations with higher PCS are considered better.

9. Pixel pairing method Definitions • - majority threshold • - region containing pixel a Pixel score calculation Pixel Correspondence Score

10. Pixel pairing Example: seg1 seg2 seg3 seg4 seg5 Compute pair data by summing how many times two pixels are given the same label. P(a,b)=3 P(a,c)=1 P(a,d)=0 P(b,c)=1 P(b,d)=0 P(c,d)=3 2. Score each segmentation according to how often it pairs pixels in the same way as the majority of other segmentations. For this example, the majority threshold is: For seg1: P(a,b) is paired and P(a,b)=3 which is greater than MT

11. Pixel pairing Example: seg1 seg2 seg3 seg4 seg5 For seg1: Final score:

12. Pixel pairing Example: seg1 seg2 seg3 seg4 seg5 For seg2: Final score:

13. Outline • Introduction to segmentation parameter optimization • Related work • Pixel pairing • Results

14. Results Segmentation method: Efficient graph based Parameters: = 1300 images Input:

15. Segmentation Output The output images shown here were selected randomly from the set of 1300. parameters input

16. Results • Images with the highest pixel correspondence score: input

17. Results Segmentation method: Mean shift Parameters: = 720 images Input:

18. Segmentation Output The output images shown here were selected randomly from the set of 720. parameters input

19. Results • Images with the highest pixel correspondence score: input

20. Results Segmentation method: Efficient graph based Parameters: = 1300 images Input:

21. Results The output images shown here were selected randomly from the set of 1300. parameters input

22. Results • Images with the highest pixel correspondence score: input

23. Conclusions • This method requires a range for parameter values as input. • The output is a set of good starting parameter values that can provide useful information about the parameter space. • The output is only as good as the input segmentation images. (If 92% of the segmentation results are completely undersegmented, this method will not work well) • The pixel pairing method is able to automatically find “good” segmentation parameters for an image with little (or no) user input.

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