Special Section on Superpixels for Image Processing and Computer Vision

Evaluation framework of superpixel methods with a global regularity measure

[+] Author Affiliations
Rémi Giraud

University of Bordeaux, Laboratoire Bordelais de Recherche en Informatique, UMR 5800, PICTURA, Talence, France

Centre National de la Recherche Scientifique, Laboratoire Bordelais de Recherche en Informatique, UMR 5800, PICTURA, Talence, France

University of Bordeaux, Institut de Mathématiques de Bordeaux, UMR 5251, Talence, France

Centre National de la Recherche Scientifique, Institut de Mathématiques de Bordeaux, UMR 5251, Talence, France

Vinh-Thong Ta

University of Bordeaux, Laboratoire Bordelais de Recherche en Informatique, UMR 5800, PICTURA, Talence, France

Centre National de la Recherche Scientifique, Laboratoire Bordelais de Recherche en Informatique, UMR 5800, PICTURA, Talence, France

Bordeaux INP, Laboratoire Bordelais de Recherche en Informatique, UMR 5800, PICTURA, Talence, France

Nicolas Papadakis

University of Bordeaux, Institut de Mathématiques de Bordeaux, UMR 5251, Talence, France

Centre National de la Recherche Scientifique, Institut de Mathématiques de Bordeaux, UMR 5251, Talence, France

J. Electron. Imaging. 26(6), 061603 (Jul 06, 2017). doi:10.1117/1.JEI.26.6.061603
History: Received April 22, 2017; Accepted June 7, 2017
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Abstract.  In the superpixel literature, the comparison of state-of-the-art methods can be biased by the nonrobustness of some metrics to decomposition aspects, such as the superpixel scale. Moreover, most recent decomposition methods allow setting a shape regularity parameter, which can have a substantial impact on the measured performances. We introduce an evaluation framework that aims to unify the comparison process of superpixel methods. We investigate the limitations of existing metrics and propose to evaluate each of the three core decomposition aspects: color homogeneity, respect of image objects, and shape regularity. To measure the regularity aspect, we propose a global regularity (GR) measure, which addresses the nonrobustness of state-of-the-art metrics. We evaluate recent superpixel methods with these criteria, at several superpixel scales and regularity levels. The proposed framework reduces the bias in the comparison process of state-of-the-art superpixel methods. Finally, we demonstrate that the proposed GR measure is correlated with the performances of various applications.

© 2017 SPIE and IS&T

Citation

Rémi Giraud ; Vinh-Thong Ta and Nicolas Papadakis
"Evaluation framework of superpixel methods with a global regularity measure", J. Electron. Imaging. 26(6), 061603 (Jul 06, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.6.061603


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