Semantic food segmentation for automatic dietary monitoring

Automatic food analysis has been an important task for not only personal dietary monitoring to treat and control health-related problems, but can also find usage at public environments such as smart restaurants where food recommendations are made based on calorie counting. In such applications a very crucial stage for correct calorie measurement is the accurate segmentation of food regions. In this work, we address semantic segmentation of food images with Deep Learning. Additionally, we explore food and non-food segmentation by getting advantage of supervised learning. Experimental results show that followed approach brings appealing results on semantic food segmentation and significantly advances on food and non-food segmentation.