  Assessing dietary intake accurately remains an open and challenging research
problem. In recent years, image-based approaches have been developed to
automatically estimate food intake by capturing eat occasions with mobile
devices and wearable cameras. To build a reliable machine-learning models that
can automatically map pixels to calories, successful image-based systems need
large collections of food images with high quality groundtruth labels to
improve the learned models. In this paper, we introduce a semi-automatic system
for online food image collection and annotation. Our system consists of a web
crawler, an automatic food detection method and a web-based crowdsoucing tool.
The web crawler is used to download large sets of online food images based on
the given food labels. Since not all retrieved images contain foods, we
introduce an automatic food detection method to remove irrelevant images. We
designed a web-based crowdsourcing tool to assist the crowd or human annotators
to locate and label all the foods in the images. The proposed semi-automatic
online food image collection system can be used to build large food image
datasets with groundtruth labels efficiently from scratch.
