A Multi-Task Learning Approach for Meal Assessment
This provides a more reliable and convenient dietary assessment tool for health monitoring, though it appears incremental as it builds on existing computer vision techniques.
The paper tackled the problem of dietary assessment by using a single RGB meal image with a multi-task learning CNN, achieving high accuracy and a massive reduction in processing time compared to state-of-the-art methods.
Key role in the prevention of diet-related chronic diseases plays the balanced nutrition together with a proper diet. The conventional dietary assessment methods are time-consuming, expensive and prone to errors. New technology-based methods that provide reliable and convenient dietary assessment, have emerged during the last decade. The advances in the field of computer vision permitted the use of meal image to assess the nutrient content usually through three steps: food segmentation, recognition and volume estimation. In this paper, we propose a use one RGB meal image as input to a multi-task learning based Convolutional Neural Network (CNN). The proposed approach achieved outstanding performance, while a comparison with state-of-the-art methods indicated that the proposed approach exhibits clear advantage in accuracy, along with a massive reduction of processing time.