Comparative Performance Analysis of Random Forest and SVM Classifiers in Food Image Recognition
Al Hassani, Oras, Yan Yi Chen, Eason, Subaramaniam, Kasthuri, Islam, Shayla and Kolandaisamy, Raenu (2026) Comparative Performance Analysis of Random Forest and SVM Classifiers in Food Image Recognition. ICAROB2026, 31. ISSN 2188-7829
Abstract
We present a system for food recognition based on artificial intelligence (AI) to classify food images into categories that can help in diet monitoring, health check, and automated meal logging. The solution employed a machine learning approach and a large dataset of food images, comparing two models in terms of performance: Random Forest (RF) and Support Vector Machine (SVM). The evaluation metrics such as accuracy, precision, recall, and F1-score were calculated to measure the performance of the models. To address this, a web-based user interface was designed to allow users to classify food images in real time and provide feedback, proving the system's effectiveness and userfriendliness.
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