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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3S3JPCH
Repositorysid.inpe.br/sibgrapi/2018/10.18.19.37
Last Update2018:10.18.19.37.50 tamirisnegri@gmail.com
Metadatasid.inpe.br/sibgrapi/2018/10.18.19.37.50
Metadata Last Update2020:02.20.22.06.50 administrator
Citation KeyBorgesGonz:2018:LoTeDe
TitleLocal Texture Descriptors for Color Texture Classification Under Varying Illumination
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size1572 KiB
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Author1 Borges, Tamiris Trevisan Negri
2 Gonzaga, Adilson
Affiliation1 University of Sao Paulo - USP and Federal Institute of Sao Paulo - IFSP
2 University of Sao Paulo - USP
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addresstamirisnegri@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-18 19:37:50 :: tamirisnegri@gmail.com -> administrator ::
2020-02-20 22:06:50 :: administrator -> :: 2018
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Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Transferable1
Tertiary TypeMaster's or Doctoral Work
KeywordsColor texture classification, local texture descriptors, varying illumination.
AbstractColor 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.
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