@Article{CarvalhoManSobComWan:2015:ImGrIm,
author = "Carvalho, L. E. and Mantelli Neto, Sylvio Luiz and Sobieranski, A.
C. and Comunello, Eros and von Wangenheim, A.",
affiliation = "{Universidade Federal de Santa Catarina (UFSC)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal
de Santa Catarina (UFSC)} and {Universidade Federal de Santa
Catarina (UFSC)} and {Universidade Federal de Santa Catarina
(UFSC)}",
title = "Improving graph-based image segmentation using non-linear color
similarity metrics",
journal = "International Journal of Image and Graphics",
year = "2015",
volume = "16",
number = "21",
month = "June",
keywords = "Felzenszwalb and Huttenlocher method, Polynomial Mahalanobis
Distance, Non-Linear Color Similarity Metrics.",
abstract = "We present a new segmentation method called Weighted Felzenszwalb
and Hutten-locher method (WFH), an improved version of the
well-known graph-based segmentation method, Felzenszwalb and
Huttenlocher method (FH). Our method uses a non-linear
discrimination function based on Polynomial Mahalanobis Distance
(PMD) as the color similarity metric. Two empirical validation
experiments were performed using as a golden standard ground
truths from a publicly available source, the Berkeley dataset, and
an objective segmentation quality measure, the Rand dissimilarity
index. In the first experiment the results were compared against
the original FH method. In the second, WFH was compared against
several well-known segmentation methods. In both cases WFH
presented significant better similarity results when compared with
the golden standard and segmentation results presented a reduction
of over-segmented regions.",
issn = "0219-4678",
language = "en",
targetfile = "carvalho_improving.pdf",
urlaccessdate = "29 mar. 2024"
}