Segmentation of Images By Color

1. Segmentation of Images By Color Using K-means Clustring Algorithm Presentation by Aviv Peled Ofir Nijinsky Introduction to Computational and Biological Vision 2012 Ben Gurion University of The Negev Your Text Here

2. Segmentation Segmentation is ill-defined In specific cases segmentation can be easy and useful

3. Example of a Restricted World Segmentation by colors is very effective A good segmentation is a crucial step in a Fruit Ninja Solver

4. Goals and Ideas • Using k-means clustering algorithm to get a segmentation by color of an image from fruit ninja • Trying to get a good segmentation • Minimizing human interaction

5. K-Means Clustering of Pixel Colors Mapping pixels to the RGB color space The “actual” k should be known in advance

6. Segmentation Example The result could be better… 

7. Estimating The K Parameter Too big k parameter decreases the minimal distance between centers

8. Running The Algorithm The estimation of k is pretty close! But the problem from the previous slide persists

9. Importance of Representations

10. לֹColor Representation What would be a good representation for our needs?

11. Running the Algorithm in HSV Original Optimal Result

12. Running the Algorithm in RGB Original Optimal Result

13. Running the Algorithm in HSV Original Optimal Result

14. Running the Algorithm in RGB Original Optimal Result

15. Running the Algorithm in RGB Original Optimal Result

16. Why Use RGB? Is HSV the ultimate answer?

17. Why Use RGB? RGB HSV