TY - GEN
T1 - A Mobile Food Recognition System for Dietary Assessment
AU - Aktı, Şeymanur
AU - Qaraqe, Marwa
AU - Ekenel, Hazım Kemal
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained unexplored. Therefore, in this paper we focus on developing a mobile friendly, Middle Eastern cuisine focused food recognition application for assisted living purposes. In order to enable a low-latency, high-accuracy food classification system, we opted to utilize the Mobilenet-v2 deep learning model. As some of the foods are more popular than the others, the number of samples per class in the used Middle Eastern food dataset is relatively imbalanced. To compensate for this problem, data augmentation methods are applied on the underrepresented classes. Experimental results show that using Mobilenet-v2 architecture for this task is beneficial in terms of both accuracy and the memory usage. With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images.
AB - Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained unexplored. Therefore, in this paper we focus on developing a mobile friendly, Middle Eastern cuisine focused food recognition application for assisted living purposes. In order to enable a low-latency, high-accuracy food classification system, we opted to utilize the Mobilenet-v2 deep learning model. As some of the foods are more popular than the others, the number of samples per class in the used Middle Eastern food dataset is relatively imbalanced. To compensate for this problem, data augmentation methods are applied on the underrepresented classes. Experimental results show that using Mobilenet-v2 architecture for this task is beneficial in terms of both accuracy and the memory usage. With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images.
KW - Assistive technology
KW - Computer vision
KW - Food recognition
UR - http://www.scopus.com/inward/record.url?scp=85135801794&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135801794&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-13321-3_7
DO - 10.1007/978-3-031-13321-3_7
M3 - Conference contribution
AN - SCOPUS:85135801794
SN - 9783031133206
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 71
EP - 81
BT - Image Analysis and Processing. ICIAP 2022 Workshops - ICIAP International Workshops, Revised Selected Papers
A2 - Mazzeo, Pier Luigi
A2 - Distante, Cosimo
A2 - Frontoni, Emanuele
A2 - Sclaroff, Stan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Image Analysis and Processing , ICIAP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -