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

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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 paperwhile assessing segmentation accuracy for historical mapswe 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.

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Bibtex (lrde.bib)

@InProceedings{	  chazalon.21.icdar.1,
  title		= {Revisiting the Coco 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,
  pages		= {},
  address	= {Lausanne, Switzerland},
  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.},
  note		= {To appear}
}