KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

This section describes main characteristics of the segment data set and its attributes:

General information

Attribute description

Attribute	Domain	Attribute	Domain
Region-centroid-col	[1.0, 254.0]	Rawred-mean	[0.0, 137.11111]
Region-centroid-row	[11.0, 251.0]	Rawblue-mean	[0.0, 150.88889]
Region-pixel-count	[9.0, 10.0]	Rawgreen-mean	[0.0, 142.55556]
Short-line-density-5	[0.0, 0.33333334]	Exred-mean	[-49.666668, 9.888889]
Short-line-density-2	[0.0, 0.22222222]	Exblue-mean	[-12.444445, 82.0]
Vedge-mean	[0.0, 29.222221]	Exgreen-mean	[-33.88889, 24.666666]
Vedge-sd	[0.0, 991.7184]	Value-mean	[0.0, 150.88889]
Hedge-mean	[0.0, 44.722225]	Saturatoin-mean	[0.0, 1.0]
Hedge-sd	[-1.5894573E-8, 1386.3292]	Hue-mean	[-3.0441751, 2.9124804]
Intensity-mean	[0.0, 143.44444]	Output	{1, 2, 3, 4, 5, 6, 7}

Additional information

This database contains instances drawn randomly from a database of 7 outdoor images (classes). The images were handsegmented to create a classification for every pixel. Each instance encodes a 3x3 region.

The task is to determine the type of surface of each region.

Attributes description:

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, and this is the mean value. This attribute is used as a vertical edge detector.
7. Vegde-sd: the contrast of horizontally adjacent pixels in the region. There are 6, and this is the standard deviation. This attribute is used as a vertical edge detector.
8. Hedge-mean: measures the contrast of vertically adjacent pixels. Used for horizontal line detection (mean)
9. Hedge-sd: measures the contrast of vertically adjacent pixels. Used for horizontal line detection (standard deviation)
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 (see James D. Foley and Andries Van Dam. 1982. Fundamentals of Interactive Computer Graphics. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA)
18. Saturatoin-mean: (see James D. Foley and Andries Van Dam. 1982. Fundamentals of Interactive Computer Graphics. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.)
19. Hue-mean: (see James D. Foley and Andries Van Dam. 1982. Fundamentals of Interactive Computer Graphics. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.)