segment-test.arff

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% ----------------------------------------------------------------------------
% This is a subset of the segmentation data (i.e. a subset of the combined 
% original training and test datasets). The description below refers
% to the original dataset. This subset has 810 instances. The distribution
% of classes is NOT stratified in this subset.
% ----------------------------------------------------------------------------
%
% 1. Title: Image Segmentation data
% 
% 2. Source Information
%    -- Creators: Vision Group, University of Massachusetts
%    -- Donor: Vision Group (Carla Brodley, [e-mail])
%    -- 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.
% 
%
%
%
%
% Relabeled values in attribute class
%    From: 1                       To: brickface           
%    From: 2                       To: sky                 
%    From: 3                       To: foliage             
%    From: 4                       To: cement              
%    From: 5                       To: window              
%    From: 6                       To: path                
%    From: 7                       To: grass               
%
@relation segment

@attribute region-centroid-col numeric
@attribute region-centroid-row numeric
@attribute region-pixel-count numeric
@attribute short-line-density-5 numeric
@attribute short-line-density-2 numeric
@attribute vedge-mean numeric
@attribute vegde-sd numeric
@attribute hedge-mean numeric
@attribute hedge-sd numeric
@attribute intensity-mean numeric
@attribute rawred-mean numeric
@attribute rawblue-mean numeric
@attribute rawgreen-mean numeric
@attribute exred-mean numeric
@attribute exblue-mean numeric
@attribute exgreen-mean numeric
@attribute value-mean numeric
@attribute saturation-mean numeric
@attribute hue-mean numeric
@attribute class {brickface,sky,foliage,cement,window,path,grass}

@data

144,35,9,0,0,2.33333,2.03306,2.05556,1.73098,37.5926,32.3333,47.4444,33,-15.7778,29.5556,-13.7778,47.4444,0.319714,-2.13876,cement
118,180,9,0,0,1.94444,1.48199,3.11111,1.08866,48.5556,44.1111,59,42.5556,-13.3333,31.3333,-18,59,0.278822,-1.99604,path
6,174,9,0,0,1.88889,1.00741,2.88889,4.02963,19.0741,15.1111,17.7778,24.3333,-11.8889,-3.88889,15.7778,24.3333,0.381867,2.39502,grass
152,220,9,0,0,0.944445,0.685185,1.44444,2.16296,14.6296,11.5556,13.1111,19.2222,-9.22222,-4.55556,13.7778,19.2222,0.416705,2.30688,grass
189,142,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,window
108,144,9,0,0,0.388889,0.151852,0.222222,0.074074,0.962963,0,2.88889,0,-2.88889,5.77778,-2.88889,2.88889,1,-2.0944,foliage
127,121,9,0,0,1.66667,0.800001,2,2.44444,23.1111,21.6667,30.1111,17.5556,-4.33333,21,-16.6667,30.1111,0.415524,-1.74074,brickface
99,198,9,0,0,5.05555,6.434,5.66667,5.79271,58.3333,51.8889,72.3333,50.7778,-19.3333,42,-22.6667,72.3333,0.299654,-2.03587,path
172,218,9,0,0,2.55556,2.42963,1.61111,2.41852,14.9259,11.8889,13.7778,19.1111,-9.11111,-3.44444,12.5556,19.1111,0.386456,2.36215,grass
233,184,9,0,0,0.5,0.077778,0.777778,0.785185,11.8519,9.77778,9.88889,15.8889,-6.22222,-5.88889,12.1111,15.8889,0.405556,2.12865,grass