Intelligent SVM based food intake measurement system

As people across the globe are becoming more interested in watching their weight, eating more healthily, and avoiding obesity, a system that can measure calories and nutrition in everyday meals can be very useful. Recently, due to ubiquity of mobile devices such as smart phones, Net books and tablets, the health monitoring applications are accessible by the patients practically all the time. A semi-automated food intake measurement application, running on a mobile device, could assist the patient to estimate his/her consumption calories. In this paper, to improve the accuracy of the current state of the art technologies, we have engaged color k-mean clustering along with color mean shift and texture segmentation schemes to get more accurate results in segmentation phase. Furthermore, the proposed system is built on food image processing techniques and uses nutritional fact tables. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it in order to measure the consumption of calorie and nutrient components. The proposed algorithm extracts important features such as shape, color, size and texture. Using various combinations of these features and adopting computational intelligence techniques, such as support vector machine, as a classifier, accurate results are achieved which are very close to the real calorie of the food.