segmentation.names

1. Title: Image Segmentation data

2. Source Information
   -- Creators: Vision Group, University of Massachusetts
   -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu)
   -- Date: November, 1990
 
3. Past Usage: None yet published

4. Relevant Information:

   The instances were drawn randomly from a database of 7 outdoor 
   images.  The images were handsegmented to create a classification
   for every pixel.  

   Each instance is a 3x3 region.

5. Number of Instances: Training data: 210  Test data: 2100

6. Number of Attributes: 19 continuous attributes

7. 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)

8. Missing Attribute Values: None

9. Class Distribution: 

   Classes:  brickface, sky, foliage, cement, window, path, grass.

   30 instances per class for training data.
   300 instances per class for test data.