Statlog (Image Segmentation)
Donated on 10/31/1990
This dataset is an image segmentation database similar to a database already present in the repository (Image segmentation database) but in a slightly different form.
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Real
# Instances
2310
# Features
19
Dataset Information
Additional 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.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| region-centroid-col | Feature | Integer | no | ||
| region-centroid-row | Feature | Integer | no | ||
| region-pixel-count | Feature | Integer | no | ||
| short-line-density-5 | Feature | Integer | no | ||
| short-line-density-2 | Feature | Integer | no | ||
| vedge-mean | Feature | Continuous | no | ||
| vedge-sd | Feature | Continuous | no | ||
| hedge-mean | Feature | Continuous | no | ||
| hedge-sd | Feature | Continuous | no | ||
| intensity-mean | Feature | Continuous | no |
0 to 10 of 20
Additional Variable 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) Classes: 1 = brickface, 2 = sky, 3 = foliage, 4 = cement, 5 = window, 6 = path, 7 = grass.
Dataset Files
| File | Size |
|---|---|
| segment.dat | 359.7 KB |
| segment.doc | 2.4 KB |
| Index | 111 Bytes |
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset statlog_image_segmentation = fetch_ucirepo(id=147) # data (as pandas dataframes) X = statlog_image_segmentation.data.features y = statlog_image_segmentation.data.targets # metadata print(statlog_image_segmentation.metadata) # variable information print(statlog_image_segmentation.variables)
Statlog (Image Segmentation) [Dataset]. (1990). UCI Machine Learning Repository. https://doi.org/10.24432/C5P01G.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.