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Robust Shape Regularity Criteria for Superpixel Evaluation


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.
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hal-01510062 , version 2 (20-05-2017)


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  • HAL Id : hal-01510062 , version 2


Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis. Robust Shape Regularity Criteria for Superpixel Evaluation. IEEE International Conference on Image Processing (ICIP'17), Sep 2017, Beijing, China. pp.3455-3459. ⟨hal-01510062⟩


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