Human-mimetic Estimation of Food Volume from a Single-View RGB Image using an AI System

Abstract

Background: It is well-known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed that allows assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage overweight, obesity and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of objective and passive dietary assessment with much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms.Method : A novel Artificial Intelligent (AI) system is proposed to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system "mentally" gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an "intelligent guess", implemented by an inner product between the probability vector and the reference volume vector.Dataset: The datasets utilized for model validation include: 1) two virtual food datasets produced by computer simulation, and 2) two real-world food datasets collected by us.Results: The average relative volumetric errors of our AI method were less than 9% on both virtual datasets, and 11.7% and 20.1% , respectively, on the two real-world food datasets.Discussion: We discuss: 1) the use of AI to estimate the "relative volume" of food in a plate, 2) the case of multiple foods in a plate, and 3) the potential of AI in advancing nutrition science.Conclusion: Our AI system is able to use the same food volume estimation strategy as the human uses.

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