Single-Stage Heavy-Tailed Food Classification

Deep learning based food image classification has enabled more accurate nutrition content analysis for image-based dietary assessment by predicting the types of food in eating occasion images. However, there are two major obstacles to apply food classification in real life applications. First, real...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 1115 - 1119
Main Authors He, Jiangpeng, Zhu, Fengqing
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Benchmark testing
Deep learning
Food classification
Head
Heavily-tailed distribution
Heavy-tailed distribution
Image classification
Image processing
Image-based dietary assessment
Single-stage
Tail
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Summary:Deep learning based food image classification has enabled more accurate nutrition content analysis for image-based dietary assessment by predicting the types of food in eating occasion images. However, there are two major obstacles to apply food classification in real life applications. First, real life food images are usually heavy-tailed distributed, resulting in severe class-imbalance issue. Second, it is challenging to train a single-stage (i.e. end-to-end) framework under heavy-tailed data distribution, which cause the over-predictions towards head classes with rich instances and under-predictions towards tail classes with rare instance. In this work, we address both issues by introducing a novel single-stage heavy-tailed food classification framework. Our method is evaluated on two heavy-tailed food benchmark datasets, Food101-LT and VFN-LT, and achieves the best performance compared to existing work with over 5% improvements for top-1 accuracy.
DOI:10.1109/ICIP49359.2023.10222925