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Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity: the case of the Felzenszwalb-Huttenlocher method

Abstract : The goal of this paper is to present an algorithm that builds a hierarchy of image seg-mentations from a class of dissimilarity criterions, the main example being the criterion proposed by Felzenszwalb and Huttenlocher which provides an observation scale. Specifically, we propose to select, for each observation scale, the largest not-too-coarse segmentation available in the hierarchy of quasi-flat zones. The resulting hierarchy is experimentally proved to be on par with the segmentation algorithm of Felzenszwalb and Huttenlocher, with the added property that it is now much easier to choose (tune) the scale of observation.
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https://hal-upec-upem.archives-ouvertes.fr/hal-01342967
Contributor : Yukiko Kenmochi <>
Submitted on : Thursday, July 7, 2016 - 11:23:56 AM
Last modification on : Wednesday, February 26, 2020 - 7:06:07 PM

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  • HAL Id : hal-01342967, version 1

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Silvio Guimarães, Yukiko Kenmochi, Jean Cousty, Zenilton Patrocinio, Laurent Najman. Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity: the case of the Felzenszwalb-Huttenlocher method. [Research Report] LIGM. 2016. ⟨hal-01342967v1⟩

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