segment

**Author**: University of Massachusetts Vision Group, Carla Brodley
**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/image+segmentation) - 1990
**Please cite**: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html)

**Image Segmentation Data Set**
The instances were drawn randomly from a database of 7 outdoor images. The images were hand-segmented to create a classification for every pixel. Each instance is a 3x3 region.

### Attribute Information

1. region-centroid-col: the column of the center pixel of the region.
2. region-centroid-row: the row of the center pixel of the region.
3. region-pixel-count: the number of pixels in a region = 9.
4. short-line-density-5: the results of a line extractoin algorithm that
counts how many lines of length 5 (any orientation) with
low contrast, less than or equal to 5, go through the region.
5. short-line-density-2: same as short-line-density-5 but counts lines
of high contrast, greater than 5.
6. vedge-mean: measure the contrast of horizontally
adjacent pixels in the region. There are 6, the mean and
standard deviation are given. This attribute is used as
a vertical edge detector.
7. vegde-sd: (see 6)
8. hedge-mean: measures the contrast of vertically adjacent
pixels. Used for horizontal line detection.
9. hedge-sd: (see 8).
10. intensity-mean: the average over the region of (R + G + B)/3
11. rawred-mean: the average over the region of the R value.
12. rawblue-mean: the average over the region of the B value.
13. rawgreen-mean: the average over the region of the G value.
14. exred-mean: measure the excess red: (2R - (G + B))
15. exblue-mean: measure the excess blue: (2B - (G + R))
16. exgreen-mean: measure the excess green: (2G - (R + B))
17. value-mean: 3-d nonlinear transformation
of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals
of Interactive Computer Graphics)
18. saturatoin-mean: (see 17)
19. hue-mean: (see 17)