Automatic seeded region growing for color image segmentation

1. Automatic seeded region growing for color image segmentation Authors: Frank Y. Shih, Shouxian Cheng Source: Image and Vision Computing, vol. 23, pp.877-886, 2005. Speaker: Shu-Fen Chiou(邱淑芬) Date: 11/21/2014

2. Outline • Introduction • Proposed method • Experimental results • Conclusion

3. Introduction • Color image segmentation polices: • Threshold • Boundary-based • Region-based • Hybrid techniques

4. Hybrid techniques • Seeding region growing (SRG) • Different merging order possibility SRG

5. Proposed method • Transform the color image from RGB to YCbCr • Automatic seed selection algorithm • Region growing • Region merging

6. Transform the color image from RGB to YCbCr • Why ? • YCbCr color space is widely used in video compression standards. (e.g. MPEG and JPEG) • The color difference of human perception can be directly expressed by Euclidean distance in the color space. • The intensity and chromatic components can be easily and independently controlled. • How ? The Cb (Cr, respectively)is the difference between the blue (red, respectively) component and a reference value

7. Automatic seed selection algorithm • Condition 1: • A seed pixel candidate must have the similarity higher than a threshold values. • Condition 2: • A seed pixel candidate must have the maximum relative Euclidean distance to its eight neighbors less than a threshold value.

8. Automatic seed selection algorithm • The similarity of a pixel to its neighbors is defined as : • Considering neighborhood, the standard deviations : • High similarity • Otsu’s method • Condition 1: • A seed pixel candidate must have the similarity higher than a threshold values.

9. Automatic seed selection algorithm • Calculate the maximum distance to its neighbors as : • of the pixel, of its neighbors • Not on the boundary • 0.05 • Condition 2: • A seed pixel candidate must have the maximum relative Euclidean distance to its eight neighbors less than a threshold value.

10. Automatic seed selection algorithm • Connected seeds are considered as one seed. Original color image the detected seeds are shown in red color

11. Region growing • The pixels that are unclassified and neighbors of at least one region, calculate the distance:

12. Region growing • red pixels are the seeds and the green pixels are the pixels in the sorted list T in a decreasing order of distances. • the white pixel is the pixel with the minimum distance to the seed regions • check its 4-neighbors

13. Region growing • If all labeled neighbors of p have a same label, set p to this label. • If the labeled neighbors of p have different labels, calculate the distances between p and all neighboring regions and classify p to the nearest region. • Then update the mean of this region, and add 4 neighbors of p, which are neither classified yet nor in T, to T in a decreasing order of distances. • Until the T is empty.

14. Region merging • Consider the color different and size of regions : • Color differentbetween two adjacent region Ri and Rj is defined as: • Size • select of the total number of pixels in an image as the threshold.

15. Region merging • We first examine the two regions having the smallest color different among others. • If < threshold, merge the two regions and re-compute the mean of the new region. • We repeat the process until no region has the distance less than the threshold. • Threshold=0.1

16. Region merging • If the sizewith number of pixels in a region is smaller than a threshold, the region is merged into its neighboring region with the smallest color difference. • This procedure is repeated until no region has size less than the threshold.

17. Experimental results JSEG algorithm.

18. Experimental results JSEG algorithm. JSEG algorithm.

19. Conclusion • We have presented an efficient segmentation algorithm for color image with automatic seed selection. • Experimental results show that we have a better results than JSPG.