#
#  Segmentation names INFO from http://www.ics.uci.edu/~mlearn/MLSummary.html
#
#  Prepared for S/R by
#     R.W. Oldford 2004
#
#
#
#   Image segmentation Database 
#   Donated by Carla Brodley 
#   Documentation status: Skimpy 
#   Not previously used in the ml literature as of 8/1991 
#   Image data described by high-level numeric-valued attributes, 7 classes 
#   Ftp Access : http://www.ics.uci.edu/~mlearn/MLSummary.html
#
#
#  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.