Abstract
Supervoxel segmentation leads to major improvements in video analysis since it generates simpler but meaningful primitives (i.e., supervoxels). Thanks to the flexibility of the Iterative Spanning Forest (ISF) framework and recent strategies introduced by the Dynamic Iterative Spanning Forest (DISF) for superpixel computation, we propose a new graph-based method for supervoxel generation by using iterative spanning forest framework, so-called ISF2SVX, based on a pipeline composed by four stages: (a) graph creation; (b) seed oversampling; (c) IFT-based superpixel delineation; and (d) seed set reduction. Moreover, experimental results show that ISF2SVX is capable of effectively describing the video’s color variation through its supervoxels, while being competitive for the remaining metrics considered.
The authors thank Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq – (PQ 310075/2019-0), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES – (Grant COFECUB 88887.191730/2018-00) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais – FAPEMIG – (Grants PPM-00006-18).
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Jerônimo, C. et al. (2021). Graph-Based Supervoxel Computation from Iterative Spanning Forest. In: Lindblad, J., Malmberg, F., Sladoje, N. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2021. Lecture Notes in Computer Science(), vol 12708. Springer, Cham. https://doi.org/10.1007/978-3-030-76657-3_29
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