Intrinsic quality analysis of binary partition trees

Jimmy Francky Randrianasoa 1 Camille Kurtz 2 Pierre Gançarski 1 Eric Desjardin 3 Nicolas Passat 3
1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 LIPADE - EA 2517 - Laboratoire d'Informatique Paris Descartes
3 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804
Abstract : The binary partition tree (BPT) is a well-known hierarchical data-structure, frequently involved image segmentation procedures. The efficiency of segmentation based on BPTs depends on the segmentation process ("how to use a BPT?"), but also on the quality of the data-structure ("how to build a BPT?"). In this article, we propose a scheme for BPT quality analysis, with the purpose of answering the latter question. It relies on the observation of the very structure of a BPT, with respect to a given ground-truth example. Our hypothesis is that such intrinsic scheme can bring relevant clues about the ability of a BPT to provide correct segmentation results. Experiments carried out on satellite images illustrate the relevance of this scheme.
Keywords : image segmentation mathematical morphology supervised quality evaluation binary partition tree
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Conference papers
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Jimmy Francky Randrianasoa, Camille Kurtz, Pierre Gançarski, Eric Desjardin, Nicolas Passat. Intrinsic quality analysis of binary partition trees. International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI), 2018, Montréal, Canada. pp.114-119. ⟨hal-01695077⟩

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