DietCam: Multi-view regular shape food recognition with a camera phone

Abstract

This paper presents an automatic multi-view food classification of a food intake assessment system on a smart phone. Food intake assessment plays important roles in obesity management, which has shown significant impacts on public healthcare. Conventional dietary record-based food intake assessment methods are not popularly applied due to their inconvenience and high reliance on human interactions. This paper presents a smart phone application, named DietCam, to recognize food intakes automatically. The major difficulties in food recognition from images come from uncertainties of food appearances and deformable nature of food especially when they are on a complex background environment. The proposed DietCam system utilizes a multi-view recognition method that separates every food by estimating the best perspective and recognizing them using a probabilistic method. The implemented DietCam system on an iPhone 4 platform showed improved performance compared with baseline methods for food recognition, with an average accuracy of 84% for the selective regular shape foods.