@InProceedings{BastosConc:2007:AuTeSe,
author = "Bastos, Lucas and Conci, Aura",
affiliation = "{Universidade Federal Fluminense} and {Universidade Federal
Fluminense}",
title = "Automatic Texture Segmentation Based on k-means Clustering and
Co-occurrence Features",
booktitle = "Proceedings...",
year = "2007",
editor = "Gon{\c{c}}alves, Luiz and Wu, Shin Ting",
organization = "Brazilian Symposium on Computer Graphics and Image Processing, 20.
(SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Haralick features,automatic texture segmentation, grey level
co-occurrence.",
abstract = "This work presents a method for automatic texture segmentation
based on k-means clustering technique and cooccurrence texture
features. A set of up to eight features were extracted from a 256
grey-level co-occurrence information. These features were used to
segment image regions regarding the textural homogeneity of its
areas. As the process of calculating co-occurrence information
demands the majority of computational time required,we propose a
new methodology based on a grey-level cooccurrence indexed list
(GLCIL) for fast element access and highly optimize this step in
the algorithm. Besides that, we compare the efficiency of the
proposed method against the GLCM and GLCLL algorithms. The GLCIL
shows to be the most efficient method in terms of computational
time. Additionally, traditional Brodatz textures and others
examples of the literature were tested to evaluate the
appropriateness and robustness of the method.",
conference-location = "Belo Horizonte, MG, Brazil",
conference-year = "7-10 Oct. 2007",
language = "en",
url = "http://sibgrapi.sid.inpe.br/ibi/6qtX3pFwXQZG2LgkFdY/Rwwsf",
targetfile = "lucasfinal.pdf",
urlaccessdate = "2025, Apr. 25"
}