Measures and meta-measures for the supervised evaluation of image segmentation
Document typeConference report
PublisherIEEE Computer Society Publications
Rights accessOpen Access
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.
CitationPont, J.; Marques, F. Measures and meta-measures for the supervised evaluation of image segmentation. A: IEEE Conference on Computer Vision and Pattern Recognition. "CVRP 2013: 2013 IEEE Conference on Computer Vision and Pattern Recognition: proceedings: 23-28 June 2013: Portland, Oregon, USA". Portland, Oregon: IEEE Computer Society Publications, 2013, p. 2131-2138.
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