Sequential transfer learning based on hierarchical clustering for improved performance in deep learning based food segmentation

Mia S. N. Siemon*, A. S. M. Shihavuddin, Gitte Ravn-Haren

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.

Original languageEnglish
Article number813
JournalScientific Reports
Volume11
Issue number1
Number of pages14
ISSN2045-2322
DOIs
Publication statusPublished - 2021

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