Muti-Stage Hierarchical Food Classification
Xinyue Pan Jiangpeng He Fengqing Zhu
Food image classification serves as a fundamental and critical step in
image-based dietary assessment, facilitating nutrient intake analysis from
captured food images. However, existing works in food classification
predominantly focuses on predicting 'food types', which do not contain direct
nutritional composition information. This limitation arises from the inherent
discrepancies in nutrition databases, which are tasked with associating each
'food item' with its respective information. Therefore, in this work we aim to
classify food items to align with nutrition database. To this end, we first
introduce VFN-nutrient dataset by annotating each food image in VFN with a food
item that includes nutritional composition information. Such annotation of food
items, being more discriminative than food types, creates a hierarchical
structure within the dataset. However, since the food item annotations are
solely based on nutritional composition information, they do not always show
visual relations with each other, which poses significant challenges when
applying deep learning-based techniques for classification. To address this
issue, we then propose a multi-stage hierarchical framework for food item
classification by iteratively clustering and merging food items during the
training process, which allows the deep model to extract image features that
are discriminative across labels. Our method is evaluated on VFN-nutrient
dataset and achieve promising results compared with existing work in terms of
both food type and food item classification.