The instances were drawn randomly from a database of 7 outdoor images. The images were hand-segmented to create a classification for every pixel.

Each instance is a 3 pixel x 3 pixel region (9 pixels). Your task is to predict the probability that each 3x3 image region belongs to each of the seven classes (window, foliage, brickface, sky, grass, path, cement).

For each unique 3x3 region you should provide 7 probabilities with value between 0 and 1.

Your submission file should look like:

ID           CLASS_0    CLASS_1    CLASS_2    CLASS_3    CLASS_4      CLASS_5     CLASS_6
<string>      <number>  <number>   <number>   <number>   <number>     <number>   <number>
 5             .034     .215       .567       .975        .123         .103       .453

Note that the images are not provided, but the attributes of each 3x3 region are. The data is split between a train and test set.

Attributes per instance are:

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. 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)

Citation Policy:

If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository:

Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Here is a BiBTeX citation as well:

@misc{Dua:2017 ,

author = "Dua, Dheeru and Karra Taniskidou, Efi",

year = "2017",

title = "{UCI} Machine Learning Repository",

url = "http://archive.ics.uci.edu/ml",

institution = "University of California, Irvine, School of Information and Computer Sciences" }