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We aim to estimate food portion size, a property that is strongly related tothe presence of food object in 3D space, from single monocular images underreal life setting. Specifically, we are interested in end-to-end estimation offood portion size, which has great potential in the field of personal healthmanagement. Unlike image segmentation or object recognition where annotationcan be obtained through large scale crowd sourcing, it is much more challengingto collect datasets for portion size estimation since human cannot accuratelyestimate the size of an object in an arbitrary 2D image without expertknowledge. To address such challenge, we introduce a real life food imagedataset collected from a nutrition study where the groundtruth food energy(calorie) is provided by registered dietitians, and will be made available tothe research community. We propose a deep regression process for portion sizeestimation by combining features estimated from both RGB and learned energydistribution domains. Our estimates of food energy achieved state-of-the-artwith a MAPE of 11.47%, significantly outperforms non-expert human estimates by27.56%.

Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation