CTADA: The design of a crowdsourcing tool for online food image identification and segmentation

Abstract

© 2018 IEEE. Measuring accurate dietary intake, the process of determining what someone eats during the course of the day is considered to be an open research problem in the nutrition and health fields. We have developed image-based tools to automatically obtain accurate estimates of what foods and how much energy/nutrients a user consumes. In this work, we present a crowdsourcing tool we designed and implemented to collect large sets of relevant online food images. This tool can be used to locate food items and obtaining groundtruth segmentation masks associated with all the foods presented in an image. We present a systematic design for a crowdsourcing tool aiming specifically for the task of online food image collection and annotations with a detailed description. The crowdsoucing tool we designed is tailored to meet the needs of building a large image dataset for developing automatic dietary assessment tools in the nutrition and health fields.