A Comparative Analysis of Color Channel-Based Feature Extraction using Machine Learning versus Deep Learning for Food Recognition

Yuita Arum  Sari; Dwi Cahya Astria  Nugraha; Sigit  Adinugroho

doi:10.52436/1.jutif.2025.6.4.5001

Authors

Yuita Arum Sari Faculty of Computer Science, Brawijaya University, Indonesia
Dwi Cahya Astria Nugraha Faculty of Computer Science, Brawijaya University, Indonesia
Sigit Adinugroho Faculty of Computer Science, Brawijaya University, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.4.5001

Keywords:

Color Channel Based Feature Extraction, Color Space, Deep Learning, Machine Learning, Food recognition, Texture Extraction

Abstract

Automated Dietary Assessment Accurate food recognition is a big challenge in computer vision which is critical for developing Automated Dietary assessment and health monitoring systems. The key question it answered was whether traditional machine learning with feature engineering by hand can beat modern deep learning approaches? In this Context, this study serves as a comparative analysis of these two paradigms. The baseline method worked by extracting texture (LBP,GLCM) and color information from different channels of five colors spaces (RGB, HSV, LAB, YUV,YCbCr) followed by feeding these features into multiple classifiers such as Nearest Neighbor(NN), Decision Tree and Naïve Bayes. These were then compared to deep learning models (MobileNet_v2, ResNet18, ResNet50, EfficientNet_B0). The best traditional one can reach an accuracy of 93.33%, using texture features extracted from the UV channel and classified with a NN. Nevertheless, the deep learning models consistently presented higher performance and MobileNet_v2 reached up to 94.9% accuracy without requiring manual feature selection. In this paper, we show that end-to-end deep learning models are more powerful and error robust for food recognition. These results highlight their promise for constructing more effective and scalable real-world applications with less need for intricate, domain-specific feature engineering.

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