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