Robust Shape Regularity Criteria for Superpixel Evaluation

Rémi Giraud 1, 2 Vinh-Thong Ta 3, 1 Nicolas Papadakis 2
1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 IMB - Institut de Mathématiques de Bordeaux
3 Bordeaux INP
Abstract : Regular decompositions are necessary for most superpixel-based object recognition or tracking applications. So far in the literature, the regularity or compactness of a superpixel shape is mainly measured by its circularity. In this work, we first demonstrate that such measure is not adapted for super-pixel evaluation, since it does not directly express regularity but circular appearance. Then, we propose a new metric that considers several shape regularity aspects: convexity, balanced repartition, and contour smoothness. Finally, we demonstrate that our measure is robust to scale and noise and enables to more relevantly compare superpixel methods.
Keywords : Superpixels Compactness Quality measure
Type de document :
Pré-publication, Document de travail
IEEE International Conference on Image Processing (ICIP). 2017
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Contributeur : Rémi Giraud <>
Soumis le : samedi 20 mai 2017 - 15:40:43
Dernière modification le : lundi 5 juin 2017 - 16:21:04

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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale 4.0 International License

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  • HAL Id : hal-01510062, version 2

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UNIV-BORDEAUX | LABRI | IMB

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Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis. Robust Shape Regularity Criteria for Superpixel Evaluation. IEEE International Conference on Image Processing (ICIP). 2017. <hal-01510062>

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