main main
KEEL-dataset - data set description
dataset/images/segment.jpg



This section describes main characteristics of the segment data set and its attributes:

General information

Image Segmentation data set
TypeClassificationOriginReal world
Features 19(Real / Integer / Nominal)(19 / 0 / 0)
Instances2310 Classes7
Missing values?No

Attribute description

AttributeDomainAttributeDomain
Region-centroid-col[1.0, 254.0]Rawred-mean[0.0, 137.11111]
Region-centroid-row[11.0, 251.0]Rawblue-mean[0.0, 150.88889]
Region-pixel-count[9.0, 10.0]Rawgreen-mean[0.0, 142.55556]
Short-line-density-5[0.0, 0.33333334]Exred-mean[-49.666668, 9.888889]
Short-line-density-2[0.0, 0.22222222]Exblue-mean[-12.444445, 82.0]
Vedge-mean[0.0, 29.222221]Exgreen-mean[-33.88889, 24.666666]
Vedge-sd[0.0, 991.7184]Value-mean[0.0, 150.88889]
Hedge-mean[0.0, 44.722225]Saturatoin-mean[0.0, 1.0]
Hedge-sd[-1.5894573E-8, 1386.3292]Hue-mean[-3.0441751, 2.9124804]
Intensity-mean[0.0, 143.44444]Output{1, 2, 3, 4, 5, 6, 7}

Additional information

This database contains instances drawn randomly from a database of 7 outdoor images. The images were handsegmented to create a classification for every pixel. Each instance encodes a 3x3 region.

The task is to determine the type of surface of each region.




In this section you can download some files related to the segment data set:

  • The complete data set already formatted in KEEL format can be downloaded from herezip.gif.
  • A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from herezip.gif.
  • A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from herezip.gif.
  • The header file associated to this data set can be downloaded from heretxt.png.
  • This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Image+Segmentation.


 
 Copyright 2004-2014, KEEL (Knowledge Extraction based on Evolutionary Learning)
About the Webmaster Team
Valid XHTML 1.1   Valid CSS!