1. Fall 2012 Longin Jan Latecki latecki@temple.edu
Image Segmentation
Fall 2012
Longin Jan Latecki

2. Image Segmentation
Segmentation divides an image into its constituent regions or objects.
Segmentation of images is a difficult task in image processing. Still under research.
Segmentation allows to extract objects in images.

3. Segmentation Algorithms
Segmentation algorithms are based on one of two basic properties of color, gray values, or texture: discontinuity and similarity.
First category is to partition an image based on abrupt changes in intensity, such as edges in an image.
Second category is based on partitioning an image into regions that are similar according to a predefined criteria.

4. Segmentation as Clustering in Color Space
1. Each image pixel (x,y) is mapped to a point in a color space, e.g.:
Color(x,y) = (R (x,y), G(x,y), B(x,y))
It is many to one mapping.
2. The points in the color space are grouped to clusters.
3. The clusters are then mapped back to regions in the image.
4. The regions (or segments or superpixels) are then usually displayed by assigning an the average color value to the pixels belonging to the same clusters.

5. Example mapping
Color(x,y) = (R (x,y), G(x,y), B(x,y))

6. Segmentation in Feature space
Mapping of pixel (x,y) to Color(x,y) = (R (x,y), G(x,y), B(x,y))
is an example of mapping image pixels to feature vectors in the feature space. We could further extend it to include pixel coordinates, e.g., to
Fv(x,y) = (R (x,y), G(x,y), B(x,y), α x, α y),
where α is a scaling parameter to normalize pixel coordinates to match the range of color values [0, 255].
Features can be also computed from regions around the pixels, e.g., each pixel could represented with a gray level histogram in its 21 x 21 neighborhood.

7. Examples Original pictures segmented pictures
Mnp: 30, percent 0.05, cluster number 4
Mnp : 20, percent 0.05, cluster number 7

8. Original Image
2 clusters
4 Clusters
6 Clusters

9. Homework:
Implement in Matlab and test on some example images the clustering in the color space.
Use Euclidean distance in RGB color space.
You can use k-mean or some other clustering algorithm.
Test images: rose, plane, car, tiger, rabbit

10. Segmentation by Thresholding
Suppose that the gray-level histogram corresponds to an image f(x,y) composed of dark objects on the light background, in such a way that object and background pixels have gray levels grouped into two dominant modes. One obvious way to extract the objects from the background is to select a threshold ‘T’ that separates these modes.
Then any point (x,y) for which f(x,y) < T is called an object point, otherwise, the point is called a background point.

11. Gray Scale Image Histogram Thresholding Example