Food Classification using Joint Representation of Visual and Textual Data

• URL: http://arxiv.org/abs/2308.02562v2
• Date: Wed, 30 Aug 2023 11:47:05 GMT
• Title: Food Classification using Joint Representation of Visual and Textual Data
• Authors: Prateek Mittal, Puneet Goyal, Joohi Chauhan
• Abstract summary: We propose a multimodal classification framework that uses the modified version of EfficientNet with the Mish activation function for image classification. The proposed network and the other state-of-the-art methods are evaluated on a large open-source dataset, UPMC Food-101.
• Score: 45.94375447042821
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Food classification is an important task in health care. In this work, we propose a multimodal classification framework that uses the modified version of EfficientNet with the Mish activation function for image classification, and the traditional BERT transformer-based network is used for text classification. The proposed network and the other state-of-the-art methods are evaluated on a large open-source dataset, UPMC Food-101. The experimental results show that the proposed network outperforms the other methods, a significant difference of 11.57% and 6.34% in accuracy is observed for image and text classification, respectively, when compared with the second-best performing method. We also compared the performance in terms of accuracy, precision, and recall for text classification using both machine learning and deep learning-based models. The comparative analysis from the prediction results of both images and text demonstrated the efficiency and robustness of the proposed approach.

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