NDLI: Multi-Task Learning for Food Identification and Analysis with Deep Convolutional Neural Networks

Content Provider	Springer Nature Link
Author	Zhang, Xi Jin Lu, Yi Fan Zhang, Song Hai
Copyright Year	2016
Abstract	In this paper, we proposed a multi-task system that can identify dish types, food ingredients, and cooking methods from food images with deep convolutional neural networks. We built up a dataset of 360 classes of different foods with at least 500 images for each class. To reduce the noises of the data, which was collected from the Internet, outlier images were detected and eliminated through a one-class SVM trained with deep convolutional features. We simultaneously trained a dish identifier, a cooking method recognizer, and a multi-label ingredient detector. They share a few low-level layers in the deep network architecture. The proposed framework shows higher accuracy than traditional method with handcrafted features, and the cooking method recognizer and ingredient detector can be applied to dishes which are not included in the training dataset to provide reference information for users.
Starting Page	489
Ending Page	500
Page Count	12
File Format	PDF
ISSN	10009000
Journal	Journal of Computer Science and Technology
Volume Number	31
Issue Number	3
e-ISSN	18604749
Language	English
Publisher	Springer US
Publisher Date	2016-05-06
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	multi-task learning convolutional neural network food recognition machine learning Computer Science Software Engineering Theory of Computation Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Information Systems Applications (incl. Internet)
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Computer Science Applications Software Hardware and Architecture

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