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 NameRoleTypeDemographicDescriptionUnitsMissing Values
classTargetCategoricalno
region-centroid-colFeatureContinuousthe column of the center pixel of the regionno
region-centroid-rowFeatureContinuousthe row of the center pixel of the regionno
region-pixel-countFeatureContinuousthe number of pixels in a region = 9no
short-line-density-5FeatureContinuousthe 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 regionno
short-line-density-2FeatureContinuoussame as short-line-density-5 but counts lines of high contrast, greater than 5no
vedge-meanFeatureContinuousmeasure 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 detectorno
vedge-sdFeatureContinuoussee 6no
hedge-meanFeatureContinuousmeasures the contrast of vertically adjacent pixels. Used for horizontal line detection.no
hedge-sdFeatureContinuoussee 8no

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

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download
27 citations
41416 views

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy