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: 2007.05.10

Outline Introduction Otsu’s method Proposed method Experiment results Conclusions

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

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.

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

The proposed method Histogram of the sample image The obtained dendrogram

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

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

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

The proposed method Dist A < Dist B Three groups Two groups Dist A 75 150 200 2 3 50

Experiment results Original images The histogram of Original images

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

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

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

Experiment results

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