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62 lines
2.4 KiB
Plaintext
Executable File
62 lines
2.4 KiB
Plaintext
Executable File
1. Title: Image Segmentation data
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2. Source Information
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-- Creators: Vision Group, University of Massachusetts
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-- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu)
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-- Date: November, 1990
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3. Past Usage: None yet published
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4. Relevant Information:
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The instances were drawn randomly from a database of 7 outdoor
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images. The images were handsegmented to create a classification
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for every pixel.
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Each instance is a 3x3 region.
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5. Number of Instances: Training data: 210 Test data: 2100
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6. Number of Attributes: 19 continuous attributes
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7. Attribute Information:
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1. region-centroid-col: the column of the center pixel of the region.
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2. region-centroid-row: the row of the center pixel of the region.
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3. region-pixel-count: the number of pixels in a region = 9.
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4. short-line-density-5: the results of a line extractoin algorithm that
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counts how many lines of length 5 (any orientation) with
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low contrast, less than or equal to 5, go through the region.
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5. short-line-density-2: same as short-line-density-5 but counts lines
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of high contrast, greater than 5.
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6. vedge-mean: measure the contrast of horizontally
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adjacent pixels in the region. There are 6, the mean and
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standard deviation are given. This attribute is used as
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a vertical edge detector.
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7. vegde-sd: (see 6)
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8. hedge-mean: measures the contrast of vertically adjacent
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pixels. Used for horizontal line detection.
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9. hedge-sd: (see 8).
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10. intensity-mean: the average over the region of (R + G + B)/3
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11. rawred-mean: the average over the region of the R value.
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12. rawblue-mean: the average over the region of the B value.
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13. rawgreen-mean: the average over the region of the G value.
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14. exred-mean: measure the excess red: (2R - (G + B))
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15. exblue-mean: measure the excess blue: (2B - (G + R))
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16. exgreen-mean: measure the excess green: (2G - (R + B))
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17. value-mean: 3-d nonlinear transformation
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of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals
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of Interactive Computer Graphics)
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18. saturatoin-mean: (see 17)
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19. hue-mean: (see 17)
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8. Missing Attribute Values: None
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9. Class Distribution:
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Classes: brickface, sky, foliage, cement, window, path, grass.
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30 instances per class for training data.
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300 instances per class for test data.
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