Revisiting the Coco Panoptic Metric to Enable Visual and Qualitative Analysis of Historical Map Instance Segmentation

Abstract

Segmentation is an important task. It is so important that there exist tens of metrics trying to score and rank segmentation systems. It is so important that each topic has its own metric because their problem is too specific. Does it? What are the fundamental differences with the ZoneMap metric used for page segmentation, the COCO Panoptic metric used in computer vision and metrics used to rank hierarchical segmentations? In this paper, while assessing segmentation accuracy for historical maps, we explain, compare and demystify some the most used segmentation evaluation protocols. In particular, we focus on an alternative view of the COCO Panoptic metric as a classification evaluation; we show its soundness and propose extensions with more “shape-oriented” metrics. Beyond a quantitative metric, this paper aims also at providing qualitative measures through precision-recall maps that enable visualizing the success and the failures of a segmentation method.

Bibtex (lrde.bib)

@InProceedings{	  chazalon.21.icdar.1,
title		= {Revisiting the {C}oco Panoptic Metric to Enable Visual and
Qualitative Analysis of Historical Map Instance
Segmentation},
author	= {Joseph Chazalon and Edwin Carlinet},
booktitle	= {Proceedings of the 16th International Conference on
Document Analysis and Recognition (ICDAR'21)},
year		= {2021},
month		= sep,
series	= {Lecture Notes in Computer Science},
publisher	= {Springer, Cham},
volume	= {12824},
pages		= {367--382},
abstract	= {Segmentation is an important task. It is so important that
there exist tens of metrics trying to score and rank
segmentation systems. It is so important that each topic
has its own metric because their problem is too specific.
Does it? What are the fundamental differences with the
ZoneMap metric used for page segmentation, the COCO
Panoptic metric used in computer vision and metrics used to
rank hierarchical segmentations? In this paper, while
assessing segmentation accuracy for historical maps, we
explain, compare and demystify some the most used
segmentation evaluation protocols. In particular, we focus
on an alternative view of the COCO Panoptic metric as a
classification evaluation; we show its soundness and
propose extensions with more shape-oriented'' metrics.
Beyond a quantitative metric, this paper aims also at
providing qualitative measures through
\emph{precision-recall maps} that enable visualizing the
success and the failures of a segmentation method.},
doi		= {10.1007/978-3-030-86337-1_25}
}