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Highly Accurate Food/Non-Food Image Classification Based on a Deep Convolutional Neural Network

  • Hokuto KagayaEmail author
  • Kiyoharu Aizawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

“Food” is an emerging topic of interest for multimedia and computer vision community. In this paper, we investigate food/non-food classification of images. We show that CNN, which is the state of the art technique for general object classification, can perform accurately for this problem. For the experiments, we used three different datasets of images: (1) images we collected from Instagram, (2) Food-101 and Caltech-256 dataset (3) dataset we used in [4]. We investigated the combinations of training and testing using the all three of them. As a result, we achieved high accuracy 96, 95 and 99% in the three datasets respectively.

Keywords

Food/Non-Food classification Convolutional neural network Deep learning 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School of Interdisciplinary Information StudiesThe University of TokyoTokyoJapan
  2. 2.Department of Information and Communication EngineeringThe University of TokyoTokyoJapan

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