Identity statement area
Reference TypeConference Proceedings
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyFerrazBorCavGonSai:2018:EvCoNe
TitleEvaluation of convolutional neural networks for raw food texture classification under variations of lighting conditions
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 02
Number of Files1
Size1768 KiB
Context area
Author1 Ferraz, Carolina Toledo
2 Borges, Tamiris T. N.
3 Cavichiolli, Adriane
4 Gonzaga, Adilson
5 Saito, José H.
Affiliation1 UNIFACCAMP
2 Federal Institute of São Paulo
3 University of São Paulo
4 University of São Paulo
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
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-22 14:04:11 :: -> administrator ::
2020-02-20 22:06:51 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Tertiary TypeWork in Progress
Keywordstexture classification, CNN, light intensity.
AbstractThis work is a preliminary evaluation of convolutional neural networks (CNN) applied to food texture classification, particularly when the texture is subject to changes in the lighting conditions. Four previously published CNN architectures (Alexnet, Resnet 18, Resnet 34 and Resnet 50) are investigated and compared to local descriptors designed specifically for this task. Although preliminary results indicate that the investigated CNN are outperformed by the descriptors, further analysis are required to investigate the impact of the experimental design adopted in this work-in-progress; especially in regard to the number of training samples and CNN configuration.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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