Robust Shape Regularity Criteria for Superpixel Evaluation

Rémi Giraud (1, 2) , Vinh-Thong Ta (1, 3) , Nicolas Papadakis (2)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 IMB - Institut de Mathématiques de Bordeaux
3 Bordeaux INP - Institut Polytechnique de Bordeaux

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.

Mots clés: Superpixels; Quality measure; Compactness

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]

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⟩