Food Image Recognition by Using Bag of SURF: Color Features

The importance of researches in the old days is the retrieval of similar objects. Content-Based Image Retrieval (CBIR) system uses low-level features such as texture, color, and shape to extract the features, but when it comes to food images it is hard to get satisfactory accurate results. Recognition of food images has recently become very important and challenging due to people's health care, religious or cultural reasons. In this paper, we propose a system that recognizes small food images consist of 10 categories by using bag of features (BoF) based on SURF detection features. In addition, we achieved up to 78% accuracy rate, and try to improve the feature detection by using color features at the same time with the SURF feature detection. This experiment shows that more accurate rate of results will be obtained than the existing methods.