segment-challenge.arff

由java开发的软件包

% ----------------------------------------------------------------------------
% 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 1,500 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

38,189,9,0,0,1,0.222222,6.22222,33.3185,29.0741,26.3333,35.2222,25.6667,-8.22222,18.4444,-10.2222,35.2222,0.271208,-2.04915,path
25,199,9,0,0,1.11111,0.607407,1.05556,0.462963,17.5185,13.1111,17.8889,21.5556,-13.2222,1.11111,12.1111,21.5556,0.393002,2.69011,grass
49,139,9,0,0,0.166667,0.077778,0.333333,0.088889,0.444444,0,1.33333,0,-1.33333,2.66667,-1.33333,1.33333,0.777778,-2.0944,foliage
63,220,9,0,0,3.05556,15.263,3.66667,6.08889,8.18519,6.55556,6.44444,11.5556,-4.88889,-5.22222,10.1111,11.5556,0.486717,2.09315,grass
161,135,9,0,0,0.055556,0.136083,0.111111,0.172133,1.25926,0.777778,3,0,-1.44444,5.22222,-3.77778,3,1,-1.82221,window
235,88,9,0,0,0.611111,0.240741,0.944445,0.32963,2.77778,0.444444,6.44444,1.44444,-7,11,-4,6.44444,0.938492,-2.26954,foliage
67,32,9,0,0,0.944444,1.06284,1.77778,1.31092,126.222,115.111,142.222,121.333,-33.3333,48,-14.6667,142.222,0.190625,-2.33375,sky
188,182,9,0,0,1.61111,0.742868,4.16667,2.12655,58,51.8889,72.4444,49.6667,-18.3333,43.3333,-25,72.4444,0.314281,-1.99159,path
217,245,9,0.111111,0.111111,3.16667,3.01662,2.16667,1.24276,9.74074,7.44444,7.11111,14.6667,-6.88889,-7.88889,14.7778,14.6667,0.572767,2.06945,grass