Abstract
Food image recognition has been extensively investigated during the last decade and had multiple useful applications for monitoring food calories and analyzing people’s eating habits to ensure better health. In this paper, we study a Vietnamese food recognition system using Convolutional Neural Networks (CNNs) based features. We manually collect one dataset for Vietnamese food classification with 13 categories and 8903 images. For learning a proper food classifier, we conduct brief analytics by comparing hand-crafted features and CNNs based features (including AlexNet, GoogleNet, ResNet50, ResNet101v2, and InceptionResnetv2) and choosing top K accuracy for measuring the performance of each model. The experimental results show that InceptionResnetv2 can achieve the best performance among all these techniques. We aim at publishing our codes and datasets for giving and additional contribution to the research community related to the Vietnamese food recognition problem.
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Acknowledgement
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number NCM2019-18-01. We would like to thank the University of Science, Vietnam National University in Ho Chi Minh City and AISIA Research Lab in Vietnam for supporting us throughout this paper.
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Ung, H.T., Dang, T.X., Thai, P.V., Nguyen, T.T., Nguyen, B.T. (2020). Vietnamese Food Recognition System Using Convolutional Neural Networks Based Features. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_37
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