@InProceedings{CasacaPaivNona:2011:SpSeUs,
author = "Casaca, Wallace and Paiva, Afonso and Nonato, Luis Gustavo",
affiliation = "{Instituto de Ci{\^e}ncias Matem{\'a}ticas e de
Computa{\c{c}}{\~a}o – USP} and {Instituto de Ci{\^e}ncias
Matem{\'a}ticas e de Computa{\c{c}}{\~a}o – USP} and {Instituto
de Ci{\^e}ncias Matem{\'a}ticas e de Computa{\c{c}}{\~a}o –
USP}",
title = "Spectral Segmentation using Cartoon-Texture Decomposition and
Inner Product-based metric",
booktitle = "Proceedings...",
year = "2011",
editor = "Lewiner, Thomas and Torres, Ricardo",
organization = "Conference on Graphics, Patterns and Images, 24. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Spectral cut, image segmentation, similarity graph,
cartoon-texture decomposition, harmonic analysis, normalized
cuts.",
abstract = "This paper presents a user-assisted image partition technique that
combines cartoon-texture decomposition, inner product-based
similarity metric, and spectral cut into a unified framework. The
cartoon-texture decomposition is used to first split the image
into textured and texture-free components, the latter being used
to define a gradient-based inner-product function. An affinity
graph is then derived and weights are assigned to its edges
according to the inner product-based metric. Spectral cut is
computed on the affinity graph so as to partition the image. The
computational burden of the spectral cut is mitigated by a
fine-to-coarse image representation process, which enables
moderate size graphs that can be handled more efficiently. The
partitioning can be steered by interactively by changing the
weights of the graph through user strokes. Weights are updated by
combining the texture component computed in the first stage of our
pipeline and a recent harmonic analysis technique that captures
waving patterns. Finally, a coarse-to-fine interpolation is
applied in order to project the partition back onto the original
image. The suitable performance of the proposed methodology is
attested by comparisons against state-of-art spectral segmentation
methods.",
conference-location = "Macei{\'o}, AL, Brazil",
conference-year = "28-31 Aug. 2011",
doi = "10.1109/SIBGRAPI.2011.34",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2011.34",
language = "en",
ibi = "8JMKD3MGPBW34M/3A3C4US",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3A3C4US",
targetfile = "Sibgrapi_paper.pdf",
urlaccessdate = "2024, Apr. 30"
}