Indigenous Food Recognition Model Based on Various Convolutional Neural Network Architectures for Gastronomic Tourism Business Analytics

Abstract

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.

Fusion of Moment Invariant Method and Deep Learning Algorithm for COVID-19 Classification

“…In both MI-based and non-MI-based experiments, the ResNet50 model had shown the worst performance compared to other models. The low ResNet50 performance could be attributed to the ResNet50 feature map size (2048 × 1 dimension) employed in this work being significantly less than the VGG16 feature map size (25,088 × 1 dimension) [10]. The larger feature map size of VGG16 compared to Custom CNN and ResNet50, on the other hand, is likely to contribute to VGG16 giving the best overall accuracy.…”

“…Machine learning is utilised to tackle a wide range of problems in a variety of industries, including phishing detection [6], water quality research [7], facial recognition [8,9], food recognition [10], and many others. The application of machine learning in the automatic detection of COVID-19 has gained extensive recognition by expediting the diagnosis and employing minimum labour inputs.…”

Optimización del posicionamiento gastronómico en Cuenca, Ecuador: evaluación técnica y diseño estratégico para el desarrollo de una aplicación móvil – fase de diagnóstico

“…En el contexto del turismo gastronómico, Razali et al (2021) se centraron en el desarrollo de un modelo para el reconocimiento de alimentos indígenas. Este estudio resalta la importancia de la identidad local en la promoción del turismo, enfatizando cómo los elementos autóctonos pueden ser un atractivo turístico clave.…”

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

“…www.ijacsa.thesai.org In particular, four pre-trained CNN model were evaluated for feature extraction, which are Efficient Net (EFFNET) [23], RESNET152 [24], NASNetMobile [25] and MobileNetV2 [26]. EFFNET has been adopted and demonstrated to have an outstanding performance in recent studies such as in the Covid-19 detection based on chest X-Ray [9], [27], smoke detection [8], fake video detection [12], pneumonia classification [10], masked face detection [7] and food recognition [28]. Meanwhile, the RESNET152 was also reported to have a good performance for scene recognition [1].…”