Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional neural networks proved to be a boon for the image classification and recognition tasks, specifically for food recognition because of the wide variety of food items. In this paper, we report experiments on food/non-food classification and food recognition using a GoogLeNet model based on deep convolutional neural network. The experiments were conducted on two image datasets created by our own, where the images were collected from existing image datasets, social media, and imaging devices such as smart phone and wearable cameras. Experimental results show a high accuracy of 99.2% on the food/non-food classification and 83.6% on the food category recognition.

Published in:
Madima'16: Proceedings Of The 2Nd International Workshop On Multimedia Assisted Dietary Management, 3-11
Presented at:
2nd International Workshop on Multimedia Assisted Dietary Management, Amsterdam, The Netherlands, 16 October 2016
New York, Assoc Computing Machinery

 Record created 2016-10-11, last modified 2018-01-28

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