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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MDPI AG
2021-08-01
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| author | Mohd Norhisham Razali Ervin Gubin Moung Farashazillah Yahya Chong Joon Hou Rozita Hanapi Raihani Mohamed Ibrahim Abakr Targio Hashem |
| author_facet | Mohd Norhisham Razali Ervin Gubin Moung Farashazillah Yahya Chong Joon Hou Rozita Hanapi Raihani Mohamed Ibrahim Abakr Targio Hashem |
| author_sort | Mohd Norhisham Razali |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T08:44:03Z |
| format | Article |
| id | doaj.art-91c70e1082b1492f81812a01d48d5188 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T08:44:03Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-91c70e1082b1492f81812a01d48d51882023-11-22T08:06:05ZengMDPI AGInformation2078-24892021-08-0112832210.3390/info12080322Indigenous Food Recognition Model Based on Various Convolutional Neural Network Architectures for Gastronomic Tourism Business AnalyticsMohd Norhisham Razali0Ervin Gubin Moung1Farashazillah Yahya2Chong Joon Hou3Rozita Hanapi4Raihani Mohamed5Ibrahim Abakr Targio Hashem6Faculty of Business Management, Universiti Teknologi Mara Cawangan Sarawak, Kota Samarahan 94350, Sarawak, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, MalaysiaFaculty of Business Management, Universiti Teknologi Mara Cawangan Sarawak, Kota Samarahan 94350, Sarawak, MalaysiaFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor Darul Ehsan, MalaysiaDepartment of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab EmiratesIn 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.https://www.mdpi.com/2078-2489/12/8/322food recognitiondeep learningtransfer learningCNNfood sentimentfood features |
| spellingShingle | Mohd Norhisham Razali 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 Information food recognition deep learning transfer learning CNN food sentiment food features |
| title | 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_short | 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 |
| topic | food recognition deep learning transfer learning CNN food sentiment food features |
| url | https://www.mdpi.com/2078-2489/12/8/322 |
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