Efficient Algorithms for Hierarchical Graph-Based Segmentation Relying on the Felzenszwalb–Huttenlocher Dissimilarity

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Cahuina, Edward Cayllahua
Cousty, Jean
Kenmochi, Yukiko
de Albuquerque Araújo, Arnaldo
Cámara-Chávez, Guillermo
Guimarães, Silvio Jamil F.
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World Scientific Pub Co Pte Lt
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Hierarchical image segmentation provides a region-oriented scale-space, i.e. a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb–Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimarães et al. proposed in 2012 a method for hierarchizing the popular Felzenszwalb–Huttenlocher method, without providing an algorithm to compute the proposed hierarchy. This paper is devoted to providing a series of algorithms to compute the result of this hierarchical graph-based image segmentation method efficiently, based mainly on two ideas: optimal dissimilarity measuring and incremental update of the hierarchical structure. Experiments show that, for an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than 4 h.
The research leading to these results has received funding from the French Agence Nationale de la Recherche, grant agreement ANR-15-CE40-0006 (CoMeDiC), the Brazilian Federal Agency of Support and Evaluation of Postgraduate Education (program CAPES/PVE: grant 064965/2014-01), the Peruvian agency Consejo Na-cional de Ciencia, Tecnolog´ıa e Innovaci´on Tecnol´ogica CONCYTEC (contract N-101-2016-. FONDECYT-DE). The first author would like to thank Brazilian agencies CNPq and CAPES and Peruvian agency CONCYTEC for the financial support during his thesis.
Palabras clave
quasi-flat zone, Image segmentation, hierarchical analysis, incremental algorithm