1. Image segmentation by histogram thresholding using hierarchical cluster analysis
Source: Pattern Recognition Letters, VOL. 27, Issue 13, October 2006
Authors: Agus Zainal Arifin, Akira Asano
Speaker: Pei-Yen Pai
Date:

2. Outline Introduction Otsu’s method Proposed method Experiment results
Conclusions

3. Introduction Image segmentation thresholding Th1 Th2 Original image
Thresholded image
Contour image

4. Otsu’s method The most common used thresholding method.
Simplicity and efficiency.
Maximize between-class variance or Minimize within-class variance.
Pci: The probability of i-th class.
Mci: The mean of i-th class.
M: The mean of image.

5. Drawback of Otsu’s method
Original image
Thresholded image
Contour image

6. The proposed method Histogram of the sample image
The obtained dendrogram

7. The proposed method
Inter-class
Intra-class
Ck1
Ck2
Ck3
255
Gray-level

8. The proposed method
Dist 1
Dist 2
Dist 3 2 3 4 5
150
200

9. The proposed method The pair of the smallest distance is Dist 2
150
200 2 3 4 5
Merge

10. The proposed method Dist A < Dist B Three groups Two groups Dist A75
150
200 2 3 50

11. Experiment results
Original images
The histogram of Original images

12. Experiment results The thesholded images by proposed method
The thesholded images by Otsu’s method

13. Experiment results The thesholded images by KI’s method
The thesholded images by Kwon’s method

14. Experiment results The thesholded images by proposed method
The ground-truth of original images

15. Experiment results

16. Conclusions Present a new gray level thresholding algorithm.
The proposed thresholding method yields better images, than those obtained by the widely used Otsu’smethod, KI’s method, and Kwon’s