@InProceedings{GalvãoFalcChow:2018:ReItSp,
author = "Galv{\~a}o, Felipe Lemes and Falc{\~a}o, Alexandre Xavier and
Chowdhury, Ananda Shankar",
affiliation = "IC-Unicamp and IC-Unicamp and {Jadavpur University}",
title = "RISF: Recursive Iterative Spanning Forest for superpixel
segmentation",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Image Foresting Transform, image segmentation, superpixel
segmentation.",
abstract = "Methods for superpixel segmentation have become very popular in
computer vision. Recently, a graph-based framework named ISF
(Iterative Spanning Forest) was proposed to obtain connected
superpixels (supervoxels in 3D) based on multiple executions of
the Image Foresting Transform (IFT) algorithm from a given choice
of four components: a seed sampling strategy, an adjacency
relation, a connectivity function, and a seed recomputation
procedure. In this paper, we extend ISF to introduce a unique
characteristic among superpixel segmentation methods. Using the
new framework, termed as Recursive Iterative Spanning Forest
(RISF), one can recursively generate multiple segmentation scales
on region adjacency graphs (i.e., a hierarchy of superpixels)
without sacrificing the efficiency and effectiveness of ISF. In
addition to a hierarchical segmentation, RISF allows a more
effective geodesic seed sampling strategy, with no negative impact
in the efficiency of the method. For a fixed number of scales
using 2D and 3D image datasets, we show that RISF can consistently
outperform the most competitive ISF-based methods.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00059",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00059",
language = "en",
ibi = "8JMKD3MGPAW/3RNQF9L",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RNQF9L",
targetfile = "89.pdf",
urlaccessdate = "2024, Apr. 29"
}