A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model

Abstract

Healthcare delivery might be completely changed by computer vision technologies. It might be used, in instance, to assess the carbohydrate content of meals for 1 diabetes patients. Based on the bag-of-features (BoF) paradigm, this paper suggests a way for automated food recognition. A thorough technical analysis was carried out to identify and optimize the components that make up the BoF architecture in order to guarantee the best outcomes. Also estimated were the associated parameters. A visual dataset with about 5000 food photos that was divided into 11 groups was developed in order to construct and test the prototype system. Using the scale-invariant 3 feature transform on the HSV colour space, the optimised method calculates dense local features. After that, it utilizes a visual dictionary with 10,000 visual words to create a linear support vector machine classifier is then used to categorize the food photos. The system demonstrated the viability of the suggested strategy in a very difficult picture dataset by achieving a classification accuracy of about 88%. By giving diabetes patients a more precise and effective means to control their carbohydrate consumption, this cutting-edge technology might have a profound effect on their quality of life.