Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2022

Abstract

Food computing has recently attracted considerable research attention due to its significance for health risk analysis. In the literature, the majority of research efforts are dedicated to food recognition. Relatively few works are conducted for food counting and segmentation, which are essential for portion size estimation. This paper presents a deep neural network, named SibNet, for simultaneous counting and extraction of food instances from an image. The problem is challenging due to varying size and shape of food as well as arbitrary viewing angle of camera, not to mention that food instances often occlude each other. SibNet is novel for proposal of learning seed map to minimize the overlap between instances. The map facilitates counting and can be completed as an instance segmentation map that depicts the arbitrary shape and size of individual instance under occlusion. To this end, a novel sibling relation sub-network is proposed for pixel connectivity analysis. Along with this paper, three new datasets covering Western, Chinese and Japanese food are also constructed for performance evaluation. The three datasets and SibNet source code are publicly available.

Keywords

Food counting, Food instance segmentation

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Food Science

Research Areas

Intelligent Systems and Optimization

Publication

Pattern Recognition

Volume

124

First Page

1

Last Page

11

ISSN

0031-3203

Identifier

10.1016/j.patcog.2021.108470

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.patcog.2021.108470

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