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)

Ryosuke Tanno made the above..
Ryosuke Tanno made the above..

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.

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Paper

PDF, 2016.

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







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