Semi-Automatic Crowdsourcing Tool for Online Food Image Collection and Annotation
This addresses the need for high-quality labeled data in food image analysis, but it is incremental as it builds on existing image-based dietary assessment methods.
The paper tackles the problem of building large, accurately labeled food image datasets for dietary intake assessment by introducing a semi-automatic system that combines web crawling, automatic food detection, and a crowdsourcing tool, resulting in efficient dataset creation from scratch.
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.