corel5k

mldr.datasets::get.mldr("corel5k")

Select your download

Partitions: select your desired partitioning strategy, validation and format

Random Stratified Iterative stratified
Hold out MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
2x5-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
10-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr

Summary

Instances 5000
Attributes 873
Inputs 499
Labels 374
Labelsets 3175
Single labelsets 2523
Max frequency 55
Cardinality 3.522
Density 0.0094
Mean IR 189.5676
SCUMBLE 0.3941
TCS 20.1999

Citation

Duygulu, P.; Barnard, K.; de Freitas, J.F.G.; Forsyth, D.A. (2002). Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. In Computer Vision, ECCV 2002, 97-112.
@incollection{,
  title="Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary",
  author="Duygulu, P. and Barnard, K. and de Freitas, J.F.G. and Forsyth, D.A.",
  year="2002",
  booktitle="Computer Vision, ECCV 2002",
  volume="2353",
  series="LNCS",
  pages="97-112"
}

Concurrence plot

In this concurrence plot, sectors represent labels and links between them depict label co-occurrences. SCUMBLE is a measure designed to assess the concurrence among imbalanced labels.