Tutorial # 9 Nov. 21,

Segmentation  Isolating a region/regions of interest in an image  Useful for:  Collect more meaningful data from an image  Easier analysis  Locate objects  Locate boundaries 2

K-means clustering 3

Example – 1 Channel  Given a gray scale image, use K-means to segment the image. Choose K = 2 (Cluster A and Cluster B)

1.) Calculate the histogram 5

2.) Initialize centroids 6

3) Cluster intensities based on distance Note: Points with intensity 2 can be classified as either, but our algorithm chooses the first cluster. 7

4.) Recalculate centroids 8

5.) Recluster intensities using new centroids We have a new clustering! Recalculate the centroid. 9

10

Final Clustering/Segmentation ABA ABA BBA This answer would change if we chose K = 3. Also, the number of iterations would change depending on the starting centroids. 12

Real example 13

Real example 14

Number of Clusters: K = 1 15

Number of Clusters: K = 3 16

Actual Segmentation 17 Cluster 1 Cluster 2 Cluster 3

Similar intensities 18

Number of Clusters: K = 5 Oversegmentation – K is too high 19

What about colour segmentation?  Different regions of interest may have the same intensity but different colours  Can use the colour information of an image to improve segmentation  Let’s focus only on the colour and remove the intensity by converting to a different colour space:  HSI (Hue Saturation Intensity)  YCbCr (Luma, Blue difference, Red difference)  L*a*b* (Lightness, a* - colour that falls on the red-green axis, b* - colour that falls on the blue-yellow axis) 20

Using L*a*b* space  Our K-means problem becomes a 2D problem  Our centroid will now have two variables, one defining the intensity of the a* channel and one defining the b* channel 3b/stats/kmeans.gif 21

22 K = 1 K = 3 K = 5

23 Cluster 1 Cluster 2 Cluster 3