1. Image Segmentation Chapter 14, David A. Forsyth and Jean Ponce, “Computer Vision: A Modern Approach”.

2. Possible approaches image segmentation as a clustering problem –Cluster together pixels that belong together image segmentation as a graph cut problem –cut a graph in sub-graphs with strong internal links and weak cut links

3. Clustering x2x2 x1x1

4. x2x2 x1x1

5. Class 1 Class 2 x2x2 x1x1 Class 3 Class 4

6. Agglomerative Clustering or Clustering by Merging Make each point a separate cluster Until the clustering is satisfactory –Merge the two cluster with the smallest inter-cluster distance

7. Divisive Clustering or Clustering by Splitting Construct a single cluster containing all points Until the clustering is satisfactory –Split the two clusters that yields the two components with the largest inter-cluster distance

8. What is a good inter-cluster distance? The distance between the closest elements (single linked clustering) Maximum distance between an element in the first cluster and another in the second (complete-link clustering) Average distance between the elements in the cluster (group average clustering)

9. How many cluster are there? data set dendrogram

10. Clustering as a minimization problem x2x2 x1x1 minimize

11. Clustering by K-Means

12. Graph theoretic clustering Represent tokens using a weighted graph. –affinity matrix Cut up this graph to get sub-graphs with strong interior links

13. Measuring Affinity Intensity Color Distance Texture

14. Scale effect on the affinity

15. Normalized Cuts Maximize the within cluster similarity compared to the across cluster difference Write graph as V, one cluster as A and the other as B where: