Conference,
Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation
(2013)
Abstract
This paper tackles the supervised evaluation of image segmentation
algorithms. First, it surveys and structures the measures used to
compare the segmentation results with a ground truth database; and
proposes a new measure: the precision-recall for objects and parts.
To compare the goodness of these measures, it defines three quantitative
meta-measures involving six state of the art segmentation methods.
The meta-measures consist in assuming some plausible hypotheses about
the results and assessing how well each measure reflects these hypotheses.
As a conclusion, this paper proposes the precision-recall curves
for boundaries and for objects-and-parts as the tool of choice for
the supervised evaluation of image segmentation. We make the datasets
and code of all the measures publicly available.
