The Image-seg dataset

The information is a replica of the notes for the segmentation dataset from the UCI repository.

1. Title: Image Segmentation data

2. Source Information

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 extraction 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. 17. value-mean: 3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics)
  18. saturation-mean: (see 17)
  19. hue-mean: (see 17)

8. Missing Attribute Values: None

9. Class Distribution:

10. Modifications for Delve

  1. The data and test files were combined and then stratified to ensure equal representation of the output classes in each of the Delve task-instance training sets.
  2. Attribute 3 (region-pixel-count) was deleted since it is a constant for this dataset.

Last Updated 11 October 1996
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