Image Segmentation
Donated on 10/31/1990
Image data described by high-level numeric-valued attributes, 7 classes
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 | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| class | Target | Categorical | no | |||
| region-centroid-col | Feature | Continuous | no | |||
| region-centroi-row | Feature | Continuous | no | |||
| region-pixel-count | Feature | Continuous | no | |||
| short-line-density-5 | Feature | Continuous | no | |||
| short-line-density-2 | Feature | Continuous | no | |||
| vedge-mean | Feature | Continuous | no | |||
| vedge-sd | Feature | Continuous | no | |||
| hedge-mean | Feature | Continuous | no | |||
| hedge-sd | 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)
Baseline Model Performance
Papers Citing this Dataset
By Johannes Brinkrolf, Christina Göpfert, Barbara Hammer. 2019
Published in Neurocomputing.
By Fabricio Breve. 2019
Published in ArXiv.
By Han Liu, Mihaela Cocea, Weili Ding. 2018
Published in Granular Computing.
By Andreas Henelius, Emilia Oikarinen, Kai Puolamaki. 2018
Published in ArXiv.
By Kai Puolamaki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl Bie. 2017
Published in ArXiv.
0 to 5 of 27
Image Segmentation. (1990). UCI Machine Learning Repository. https://doi.org/10.24432/C5GP4N.
@misc{misc_image_segmentation_50,
title = {{Image Segmentation}},
year = {1990},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5GP4N}
}pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset image_segmentation = fetch_ucirepo(id=50) # data (as pandas dataframes) X = image_segmentation.data.features y = image_segmentation.data.targets # metadata print(image_segmentation.metadata) # variable information print(image_segmentation.variables)
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.