Food Detection and Recognition Using Convolutional Neural Network

By Hokuto Kagaya, Kiyoharu Aizawa, Makoto Ogawa. ACM Multimedia, 2014

295 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Plant Science

View Paper: https://www.semanticscholar.org/paper/0373442fb84f7783f3453c46f8cccc12d184dbbc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Yoshiyuki Kawano
2. Keiji Yanai
3. Chong-Wah Ngo
4. Jingjing Chen

In Computer Vision and Pattern Recognition

1. Yoshiyuki Kawano
2. Keiji Yanai
3. Shuqiang Jiang
4. Yong Rui
5. Ramesh C. Jain
6. Weiqing Min
7. Linhu Liu
8. Chong-Wah Ngo

In Plant Science

1. Keiji Yanai
2. Yoshiyuki Kawano

Citations in the corpus

• Online Continual Learning For Visual Food Classification (2021, unknown): “Later, the modern deep learning models have been widely applied as backbone network to extract more class-discriminative features as in [17, 37, 28, 32, 10, 8, 31, 36], which significantly improves the performance.”
• Food image recognition using deep convolutional network with pre-training and fine-tuning (2015, methodology): “[2] trained a DCNN from scratch without employing pre-training and fine-tuning.”
• A Survey on Food Computing (2018, methodology): “For example, [Kagaya et al. 2014] applied the AlexNet network [Krizhevsky et al. 2012] to extract visual features for food recognition.”