Real-time calorie extraction and cuisine classification through food-image recognition

초록

The self-monitoring of user-activity tracking is the most common method of both individuals and most health-care systems. A growing demand for services that can easily monitor food and calorie information through the use of food photographs has also emerged. In this paper, an existing food-recognition and calorie-extraction system is combined with a context-recognition system that recognizes the meal type. To recognize the food images, a preceding Tensorflow-based machine-learning process was performed, and an Expert.js-based semantic network was constructed for the meal-type recognition. The food-recognition accuracy rate is 55.3%, and Korean, Chinese, and Italian food were recognized. As a result, the objective is the combining of the context awareness with the existing self-monitoring systems to enable the user to implement dietary adjustments.

키워드