Image Segmentation

Donated on 10/31/1990

Image data described by high-level numeric-valued attributes, 7 classes

Dataset Characteristics


Subject Area


Associated Tasks


Feature Type


# Instances


# Features


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?


Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values

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

Differential privacy for learning vector quantization

By Johannes Brinkrolf, Christina Göpfert, Barbara Hammer. 2019

Published in Neurocomputing.

Multi-task learning for intelligent data processing in granular computing context

By Han Liu, Mihaela Cocea, Weili Ding. 2018

Published in Granular Computing.

Human-Guided Data Exploration

By Andreas Henelius, Emilia Oikarinen, Kai Puolamaki. 2018

Published in ArXiv.

Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach

By Kai Puolamaki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl Bie. 2017

Published in ArXiv.

0 to 5 of 27

27 citations


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