Category and Ingredients Estimation From Food Image Robust to Typicality of Used Ingredients

Abstract

With the growing demand for dietary control in health management, studies that estimate categories and ingredients from food images have been conducted. Especially, recent studies have achieved high recognition accuracy through multi-task learning of categories and ingredients. Although the accuracy for frequently used ingredients in each category (typical ingredients) is high, the accuracy for infrequently used ones (untypical ingredients) has room for improvement. This paper proposes a method to improve the estimation accuracy of untypical ingredients in multi-task learning by estimating typical and untypical ingredients in separate modules. Experimental results on three datasets show the effectiveness of the proposed method in terms of the accuracy for atypical ingredients.