Skip to main content

Research Repository

Advanced Search

Deep Convolutional Generative Adversarial Network-Based Food Recognition Using Partially Labeled Data

Mandal, Bappaditya; Puhan, Niladri B.; Verma, Avijit

Deep Convolutional Generative Adversarial Network-Based Food Recognition Using Partially Labeled Data Thumbnail


Authors

Niladri B. Puhan

Avijit Verma



Abstract

Traditional machine learning algorithms using hand-crafted feature extraction techniques (such as local binary pattern) have limited accuracy because of high variation in images of the same class (or intraclass variation) for food recognition tasks. In recent works, convolutional neural networks (CNNs) have been applied to this task with better results than all previously reported methods. However, they perform best when trained with large amount of annotated (labeled) food images. This is problematic when obtained in large volume, because they are expensive, laborious, and impractical. This article aims at developing an efficient deep CNN learning-based method for food recognition alleviating these limitations by using partially labeled training data on generative adversarial networks (GANs). We make new enhancements to the unsupervised training architecture introduced by Goodfellow et al. , which was originally aimed at generating new data by sampling a dataset. In this article, we make modifications to deep convolutional GANs to make them robust and efficient for classifying food images. Experimental results on benchmarking datasets show the superiority of our proposed method, as compared to the current state-of-the-art methodologies, even when trained with partially labeled training data.

Journal Article Type Article
Acceptance Date Dec 6, 2018
Publication Date 2019-02
Publicly Available Date Mar 29, 2024
Journal IEEE Sensors Letters
Print ISSN 2475-1472
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 3
Issue 2
Article Number ARTN 7000104
DOI https://doi.org/10.1109/LSENS.2018.2886427
Keywords machine learning, algorithms, convolutional neural networks, generative adversarial networks
Publisher URL https://doi.org/10.1109/LSENS.2018.2886427

Files




You might also like



Downloadable Citations