Abstract：
:Accurately segmenting foods from optical images is a challenging task, yet becoming possible with the help of recent advances in Deep Learning based solutions. Automated identification of food items opens up possibilities of useful applications like nutrition intake monitoring. Given large variations in food choices, Deep Learning based solutions still struggle to generate human level accuracy. In this work, we propose a novel Sequential Transfer Learning method using Hierarchical Clustering. This novel approach simulates a step by step problem solving framework based on clustering of similar types of foods. The proposed approach provides up to 6% gain in accuracy compared to traditional network training and generated a robust model performing better in challenging unseen cases. This approach is also tested for segmenting foods in Danish school children meals for dietary intake monitoring as an application.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Siemon MSN,Shihavuddin ASM,Ravn-Haren Gdoi
10.1038/s41598-020-79677-1subject
Has Abstractpub_date
2021-01-12 00:00:00pages
813issue
1issn
2045-2322pii
10.1038/s41598-020-79677-1journal_volume
11pub_type
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