1. Region Segmentation

2. Find sets of pixels, such that All pixels in region i satisfy some constraint of similarity.

3. K-Means Choose a fixed number of clusters Choose cluster centers and point-cluster allocations to minimize error can’t do this by search, because there are too many possible allocations. Algorithm fix cluster centers; allocate points to closest cluster fix allocation; compute best cluster centers x could be any set of features for which we can compute a distance (careful about scaling)

4. K-Means

5. Image Segmentation by K-Means Select a value of K Select a feature vector for every pixel (color, texture, position, or combination of these etc.) Define a similarity measure between feature vectors (Usually Euclidean Distance). Apply K-Means Algorithm. Apply Connected Components Algorithm. Merge any components of size less than some threshold to an adjacent component that is most similar to it.

6. K-means clustering using intensity alone and color alone Image Clusters on intensity Clusters on color

7. K-means using color alone, 11 segments Image Clusters on color

8. K-means using color alone, 11 segments.

9. K-means using colour and position, 20 segments

10. How to find K Use prior knowledge about image. Apply the algorithm for different values of K and test for goodness of clusters. Analyze Image Histograms.

11. How to find K

13. How to Find K Realistic Histograms

14. How to Find K Smooth Histogram. (Convolve by averaging or Gaussian Filter)

15. How to Find K Find Peaks and Valleys and perform peakiness test.

16. Agglomerative clustering Assume that each cluster is single pixel (i.e. every pixel is a cluster itself). Merge Clusters i.e. attach closest to cluster it is closest to (if possible) Repeat step 2 until no more clusters can be merged.

17. Divisive clustering Assume that whole image is a single cluster. Split Clusters along best boundary (if exists) Repeat step 2 until no more clusters can be split.

18. Inter-Cluster distance single-link clustering: Minimum distance between an element of the first cluster and one of the second.. complete-link clustering: Maximum distance between an element of the first cluster and one of the second. group-average clustering: Average of distances between elements in the clusters.

19. Segmentation by Split and Merge Start with an initial segmentation (for example by K- Means). Define a criteria P for goodness of region such that P( R )=True, if R satisfies the criteria P( R )=False, otherwise For each region R, split R in four regions (quadrants), if P( R ) = False Merge any two adjacent regions R and Q if Repeat until no more clusters can be split or merged.

20. Segmentation by Split and Merge

21. Graph Theoretical Techniques for Image Segmentation

22. Graph A graph G(V,E) is a triple consisting of a vertex set V(G) an edge set E(G) and a relation that associates with each edge two vertices called its end points.

23. Path A path is a sequence of edges e1, e2, e3, … en. Such that each (for each i>2 & i<n) edge ei is adjacent to e(i+1) and e(i-1). e1 is only adjacent to e2 and en is only adjacent to e(n-1)

24. Connected & Disconnected Graph A graph G is connected if there is a path from every vertex to every other vertex in G. A graph G that is not connected is called disconnected graph.

25. Graphs Representations a e d c b Adjacency Matrix: W

26. Weighted Graphs and Their Representations a e d c b Weight Matrix: W 6

27. Minimum Cut A cut of a graph G is the set of edges S such that removal of S from G disconnects G. Minimum cut is the cut of minimum weight, where weight of cut is given as

28. Minimum Cut and Clustering

29. Image Segmentation & Minimum Cut Image Pixels Pixel Neighborhood w Similarity Measure Minimum Cut

30. Minimum Cut There can be more than one minimum cut in a given graph All minimum cuts of a graph can be found in polynomial time 1. 1 H. Nagamochi, K. Nishimura and T. Ibaraki, “Computing all small cuts in an undirected network. SIAM J. Discrete Math. 10 (1997) 469-481.

31. Drawbacks of Minimum Cut Weight of cut is directly proportional to the number of edges in the cut. Ideal Cut Cuts with lesser weight than the ideal cut

32. Normalized Cuts 1 Normalized cut is defined as N cut (A,B) is the measure of dissimilarity of sets A and B. Minimizing N cut (A,B) maximizes a measure of similarity within the sets A and B 1 J. Shi and J. Malik, “Normalized Cuts & Image Segmentation,” IEEE Trans. of PAMI, Aug 2000.

33. Finding Minimum Normalized-Cut Finding the Minimum Normalized-Cut is NP-Hard. Polynomial Approximations are generally used for segmentation

34. Finding Minimum Normalized-Cut Image Pixels Pixel Neighborhood w Similarity Measure 1 n 3 2 n-1

35. Finding Minimum Normalized-Cut

36. It can be shown that such that If y is allowed to take real values then the minimization can be done by solving the generalized eigenvalue system Finding Minimum Normalized-Cut

37. Algorithm Compute matrices W & D Solve for eigen vectors with the smallest eigen values Use the eigen vector with second smallest eigen value to bipartition the graph Recursively partition the segmented parts if necessary.

38. Figure from “Image and video segmentation: the normalised cut framework”, by Shi and Malik, 1998

39. F igure from “Normalized cuts and image segmentation,” Shi and Malik, 2000

42. Drawbacks of Minimum Normalized Cut Huge Storage Requirement and time complexity Bias towards partitioning into equal segments Have problems with textured backgrounds

43. Suggested Reading Chapter 14, David A. Forsyth and Jean Ponce, “Computer Vision: A Modern Approach”. Jianbo Shi, Jitendra Malik, “Normalized Cuts and Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997 Chapter 3, Mubarak Shah, “Fundamentals of Computer Vision”