%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2018/10.18.19.37
%2 sid.inpe.br/sibgrapi/2018/10.18.19.37.50
%T Local Texture Descriptors for Color Texture Classification Under Varying Illumination
%D 2018
%A Borges, Tamiris Trevisan Negri,
%A Gonzaga, Adilson,
%@affiliation University of Sao Paulo - USP and Federal Institute of Sao Paulo - IFSP
%@affiliation University of Sao Paulo - USP
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%C Foz do Iguaçu, PR, Brazil
%8 29 Oct.-1 Nov. 2018
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K Color texture classification, local texture descriptors, varying illumination.
%X 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.
%@language en
%3 CameraReady_Tamiris.pdf