Cafeteria Vision Identification and Amount Measurement of Foods in a Plate

We present a prototype of automatic dish recognition system, intended to ease the checkout process in self-serve cafeterias, where the price is based on what food and how much a customer takes. When a plate with several dishes in it goes though the checkout counter, a snapshot will be taken and then sent to a computer for analysis. A variety of features, including color and texture are extracted. Color texton histogram is empirically proved to be the best effective feature. Finally, support vector machines are used to classify the dishes against the pre-trained dish image bank. A preliminary system recognizing 24 dishes is constructed and several major difficulties are also identified.