Personalised food classifier and nutrition interpreter multimedia tool using deep learning

Sundarramurthi, M and Nihar, M and Giridharan, A (2020) Personalised food classifier and nutrition interpreter multimedia tool using deep learning. In: IEEE Region 10 Annual International Conference, Proceedings/TENCON, 16-19 November 2020, Virtual, Osaka; Japan, pp. 881-884.

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Abstract

Food plays a vital role in our day-to-day life to get all the required nutrients for a healthy lifestyle. In recent years, obesity has become one of the major concerns among humans. Therefore, it is necessary for each individual to keep track of the nutrition intake in order to have a balanced diet. This has scaled up the implementation of automatic food analysis and semantic food detection using different image classification approaches, among which Deep Learning has brought a series of breakthroughs in this field. We have proposed the Food Classifier and Nutrition Interpreter (FCNI), a user-friendly tool that classifies various food types with a different graphical representation of food nutrients values in terms of calorie estimation along with a multimedia audio response. FCNI improves state-of-the-art food detection by a considerable margin on achieving about 96.81 accuracy. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	cited By 0; Conference of 2020 IEEE Region 10 Conference, TENCON 2020 ; Conference Date: 16 November 2020 Through 19 November 2020; Conference Code:166041
Keywords:	Nutrients; Nutrition; Semantics, Calorie estimations; Classification approach; Food detection; Graphical representations; Healthy lifestyles; Multimedia tool; State of the art; User-friendly tool, Deep learning
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	02 Feb 2021 11:42
Last Modified:	02 Feb 2021 11:42
URI:	http://eprints.iisc.ac.in/id/eprint/67720

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