Utilizing Color Space Information as Features for Deep Learning for a Heterogeneous Food Recognition System

Izyan Uzma Binti Shamsu, Ervin Gubin Moung, Ali Farzamnia, Tiong Lin Rui, Farashazillah Yahya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Food recognition serves as one of the most promising applications of visual object recognition, as it can be used to calculate food calories and analyze people's eating habits and dietary practices for health-care purposes. It forms the primary and most vital step in developing an application capable of providing nutritional recommendations. In this work, the CNN, ResNet, and AlexNet architectures have been proposed, given their popularity among researchers. A merged dataset, comprising Food-101 and UEC-Food256, was employed to evaluate the proposed models. To ascertain the optimal color spaces for food recognition, eight selection criteria were proposed. Among the various color space selection criteria, RGB showed superior performance when used with the Custom CNN model. To enhance performance, an upsampling method was implemented to increase the number of samples in the minority classes. This was achieved by duplicating existing samples using an augmentation process, thereby balancing the merged dataset. The final results suggest that EfficientNetB0, a CNN-based pre-trained model, performs better with the RGB color space, increasing accuracy from 40.48% (Custom CNN) to 73.11% (EfficientNetB0).

Original languageEnglish
Title of host publicationProceedings of the 13th National Technical Seminar on Unmanned System Technology 2023
Subtitle of host publicationNUSYS 2023
EditorsZainah Md. Zain, Zool Hilmi Ismail, Huiping Li, Xianbo Xiang, Rama Rao Karri
PublisherSpringer Singapore
Pages167-180
Number of pages14
Volume1184
ISBN (Electronic)9789819720279
ISBN (Print)9789819720262, 9789819720293
DOIs
Publication statusPublished - 17 Sep 2024
Event13th National Technical Symposium on Unmanned System Technology - Penang, Malaysia
Duration: 2 Oct 20233 Oct 2023
Conference number: 13

Publication series

NameLecture Notes in Electrical Engineering
PublisherSpringer
Volume1184 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference13th National Technical Symposium on Unmanned System Technology
Abbreviated titleNUSYS 2023
Country/TerritoryMalaysia
CityPenang
Period2/10/233/10/23

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