@InProceedings{BorgesGonz:2018:LoTeDe,
author = "Borges, Tamiris Trevisan Negri and Gonzaga, Adilson",
affiliation = "{University of Sao Paulo - USP and Federal Institute of Sao Paulo
- IFSP} and {University of Sao Paulo - USP}",
title = "Local Texture Descriptors for Color Texture Classification Under
Varying Illumination",
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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Color texture classification, local texture descriptors, varying
illumination.",
abstract = "Color texture classification under varying illumination remains a
challenge in the field of computer vision, and it greatly relies
on the efficiency of the feature descriptors. The aim of the
thesis is to improve the classification of color texture acquired
with varying illumination sources by improving the description
power of feature descriptors. We propose three new color texture
descriptors, namely: the Opponent Color Local Mapped Pattern
(OCLMP), which combines a local methodology (LMP) with the
opponent-colors theory; the Color Intensity Local Mapped Pattern
(CILMP), which extracts color and texture information jointly, in
a multi-resolution fashion and the Extended Color Local Mapped
Pattern (ECLMP), which applies two operators to extract color and
texture information jointly as well. As the proposed methods are
based on the LMP algorithm, they are parametric functions. Finding
the optimal set of parameters for the descriptor can be a
cumbersome task. Therefore, this work adopts genetic algorithms to
automatically adjust the parameters. The methods were assessed
using two texture data sets acquired under varying illumination
sources: RawFooT (Raw Food Texture Database), and the KTH-TIPS-2b
(Textures under varying Illumination, Pose and Scale Database).
The experimental results show that the proposed descriptors are
more robust to variations to the illumination source than other
methods found in the literature. The improvement on the accuracy
was higher than 15% in the RawFoot data set, and higher than 4% in
the KTH-TIPS-2b data set.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
language = "en",
ibi = "8JMKD3MGPAW/3S3JPCH",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3S3JPCH",
targetfile = "CameraReady_Tamiris.pdf",
urlaccessdate = "2024, Apr. 29"
}