Summary

(No information yet)

License

unknown (from Weka repository)

Dependencies

Tags

arff slurped Weka

Attribute Types

Integer,Floating Point,String

Download

# Instances: 2310 / # Attributes: 20
HDF5 (398.8 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

segment

Version mldata

0

Comment

1. Title: Image Segmentation data

2. Source Information -- Creators: Vision Group, University of Massachusetts -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu) -- 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.

1. Number of Instances: Training data: 210 Test data: 2100

2. Number of Attributes: 19 continuous attributes

3. 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)

4. Missing Attribute Values: None

5. 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

Names

region-centroid-col,region-centroid-row,region-pixel-count,short-line-density-5,short-line-density-2,vedge-mean,vegde-sd,hedge-mean,hedge-sd,intensity-mean,

Types

1. numeric
2. numeric
3. numeric
4. numeric
5. numeric
6. numeric
7. numeric
8. numeric
9. numeric
10. numeric

Data (first 10 data points)

regi...	regi...	regi...	shor...	shor...	vedg...	vegd...	hedg...	hedg...	inte...	...
218	178	9	0	0	0.83...	0.54...	1.11...	0.54...	59.6...	...
113	130	9	0	0	0.27...	0.25...	0.33...	0.36...	0.88...	...
202	41	9	0	0	0.94...	0.77...	1.11...	1.0256	123....	...
32	173	9	0	0	1.72...	1.78...	9.0	6.74...	43.5...	...
61	197	9	0	0	1.44...	1.51...	2.61...	1.92...	49.5...	...
149	185	9	0	0	1.55...	1.06...	3.05...	1.92...	49.3...	...
197	229	9	0	0	1.38...	1.57...	1.16...	0.56...	17.7...	...
29	111	9	0	0	0.38...	0.24...	0.61...	0.15...	5.40...	...
1	81	9	0	0	12.1...	267....	9.22...	205....	21.3...	...
69	85	9	0	0	3.11...	8.20...	3.94...	9.44...	21.4...	...
...	...	...	...	...	...	...	...	...	...	...

Description

A jarfile containing 37 classification problems, originally obtained from the UCI repository (datasets-UCI.jar, 1,190,961 Bytes).

URLs

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Publications

Data Source

http://www.ics.uci.edu/~mlearn/MLRepository.html

Measurement Details

Usage Scenario

revision 1

by mldata on 2010-11-06 09:57

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