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Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics

In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This research undertakes...

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Main Authors: Mohd Norhisham Razali @ Ghazali, Ervin Gubin Moung, Farashazillah Yahya, Chong, Joon Hou, Rozita Hanapi, Raihani Mohamed, Ibrahim Abakr Targio Hashem
Format: Article
Language:English
English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2021
Subjects:
G154.9-155.8 Travel and state. Tourism
TX341-641 Nutrition. Foods and food supply
Online Access:https://eprints.ums.edu.my/id/eprint/32566/1/Indigenous%20food%20recognition%20model%20based%20on%20various%20convolutional%20neural%20network%20architectures%20for%20gastronomic%20tourism%20business%20analytics.pdf
https://eprints.ums.edu.my/id/eprint/32566/2/Indigenous%20food%20recognition%20model%20based%20on%20various%20convolutional%20neural%20network%20architectures%20for%20gastronomic%20tourism%20business%20analytics%20_ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32566/
https://www.mdpi.com/2078-2489/12/8/322/htm
https://doi.org/10.3390/info12080322
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spelling my.ums.eprints.325662022-05-18T03:52:12Z https://eprints.ums.edu.my/id/eprint/32566/ Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics Mohd Norhisham Razali @ Ghazali Ervin Gubin Moung Farashazillah Yahya Chong, Joon Hou Rozita Hanapi Raihani Mohamed Ibrahim Abakr Targio Hashem G154.9-155.8 Travel and state. Tourism TX341-641 Nutrition. Foods and food supply In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This research undertakes an empirical evaluation of recent transfer learning models for deep learning feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is implemented into a web application system as an attempt to automate food recognition. In this model, a fully connected layer with 11 and 10 Softmax neurons is used as the classifier for food categories in both datasets. Six pre-trained Convolutional Neural Network (CNN) models are evaluated as the feature extractors to extract essential features from food images. From the evaluation, the research found that the EfficientNet feature extractor-based and CNN classifier achieved the highest classification accuracy of 94.01% on the Sabah Food Dataset and 86.57% on VIREO-Food172 Dataset. EFFNet as a feature representation outperformed Xception in terms of overall performance. However, Xception can be considered despite some accuracy performance drawback if computational speed and memory space usage are more important than performance. Multidisciplinary Digital Publishing Institute (MDPI) 2021 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32566/1/Indigenous%20food%20recognition%20model%20based%20on%20various%20convolutional%20neural%20network%20architectures%20for%20gastronomic%20tourism%20business%20analytics.pdf text en https://eprints.ums.edu.my/id/eprint/32566/2/Indigenous%20food%20recognition%20model%20based%20on%20various%20convolutional%20neural%20network%20architectures%20for%20gastronomic%20tourism%20business%20analytics%20_ABSTRACT.pdf Mohd Norhisham Razali @ Ghazali and Ervin Gubin Moung and Farashazillah Yahya and Chong, Joon Hou and Rozita Hanapi and Raihani Mohamed and Ibrahim Abakr Targio Hashem (2021) Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics. Information, 12 (322). pp. 1-24. ISSN 2078-2489 https://www.mdpi.com/2078-2489/12/8/322/htm https://doi.org/10.3390/info12080322
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic G154.9-155.8 Travel and state. Tourism
TX341-641 Nutrition. Foods and food supply
spellingShingle G154.9-155.8 Travel and state. Tourism
TX341-641 Nutrition. Foods and food supply
Mohd Norhisham Razali @ Ghazali
Ervin Gubin Moung
Farashazillah Yahya
Chong, Joon Hou
Rozita Hanapi
Raihani Mohamed
Ibrahim Abakr Targio Hashem
Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics
description In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This research undertakes an empirical evaluation of recent transfer learning models for deep learning feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is implemented into a web application system as an attempt to automate food recognition. In this model, a fully connected layer with 11 and 10 Softmax neurons is used as the classifier for food categories in both datasets. Six pre-trained Convolutional Neural Network (CNN) models are evaluated as the feature extractors to extract essential features from food images. From the evaluation, the research found that the EfficientNet feature extractor-based and CNN classifier achieved the highest classification accuracy of 94.01% on the Sabah Food Dataset and 86.57% on VIREO-Food172 Dataset. EFFNet as a feature representation outperformed Xception in terms of overall performance. However, Xception can be considered despite some accuracy performance drawback if computational speed and memory space usage are more important than performance.
format Article
author Mohd Norhisham Razali @ Ghazali
Ervin Gubin Moung
Farashazillah Yahya
Chong, Joon Hou
Rozita Hanapi
Raihani Mohamed
Ibrahim Abakr Targio Hashem
author_facet Mohd Norhisham Razali @ Ghazali
Ervin Gubin Moung
Farashazillah Yahya
Chong, Joon Hou
Rozita Hanapi
Raihani Mohamed
Ibrahim Abakr Targio Hashem
author_sort Mohd Norhisham Razali @ Ghazali
title Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics
title_short Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics
title_full Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics
title_fullStr Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics
title_full_unstemmed Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics
title_sort indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics
publisher Multidisciplinary Digital Publishing Institute (MDPI)
publishDate 2021
url https://eprints.ums.edu.my/id/eprint/32566/1/Indigenous%20food%20recognition%20model%20based%20on%20various%20convolutional%20neural%20network%20architectures%20for%20gastronomic%20tourism%20business%20analytics.pdf
https://eprints.ums.edu.my/id/eprint/32566/2/Indigenous%20food%20recognition%20model%20based%20on%20various%20convolutional%20neural%20network%20architectures%20for%20gastronomic%20tourism%20business%20analytics%20_ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32566/
https://www.mdpi.com/2078-2489/12/8/322/htm
https://doi.org/10.3390/info12080322
_version_ 1760231043644784640
score 13.211869

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