DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System
Ryosuke Tanno Koichi Okamoto Keiji Yanai
Department of Informatics, The University of Electro-Communication
Proc. of ACM MM Workshop on Multimedia Assisted Dietary Management (MADiMa 2016)
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Ryosuke Tanno made the above..
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Ryosuke Tanno made the above..
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Abstract
Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for mobile devices where memory and computational power is limited.
Paper
Citation
Ryosuke Tanno, Koichi Okamoto and Keiji Yanai. "DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System", In Proc. of ACM MM Workshop on Multimedia Assisted Dietary Management (MADiMa), 2016. Bibtex