NDLI: Mid-level deep Food Part mining for food image recognition

Content Provider	IET Digital Library
Author	Zheng, Jiannan Zou, Liang Wang, Z. Jane
Abstract	There has been a growing interest in food image recognition for a wide range of applications. Among existing methods, mid-level image part-based approaches show promising performances due to their suitability for modelling deformable food parts (FPs). However, the achievable accuracy is limited by the FP representations based on low-level features. Benefiting from the capacity to learn powerful features with labelled data, deep learning approaches achieved state-of-the-art performances in several food image recognition problems. Both mid-level-based approaches and deep convolutional neural networks (DCNNs) approaches clearly have their respective advantages, but perhaps most importantly these two approaches can be considered complementary. As such, the authors propose a novel framework to better utilise DCNN features for food images by jointly exploring the advantages of both the mid-level-based approaches and the DCNN approaches. Furthermore, they tackle the challenge of training a DCNN model with the unlabelled mid-level parts data. They accomplish this by designing a clustering-based FP label mining scheme to generate part-level labels from unlabelled data. They test on three benchmark food image datasets, and the numerical results demonstrate that the proposed approach achieves competitive performance when compared with existing food image recognition approaches.
Starting Page	298
Ending Page	304
Page Count	7
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 3, Apr (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0335
Journal	IET Computer Vision
Publisher Date	2017-11-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Benchmark Food Image Datasets Clustering-based FP Label Mining Scheme Computer Vision And Image Processing Technique Data Handling Technique Data Mining DCNN Features Deep Convolutional Neural Network Deep Learning Approach Deformable Food Parts Modelling Feature Extraction Feedforward Neural Network Food Image Recognition FP Representations Image Recognition Image Representation Knowledge Engineering Technique Learning in AI Midlevel Deep FP Mining Midlevel Image Part-based Approach Neural Computing Technique Part-level Labels Generation Unlabelled Data
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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