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Food recognition and calorie extraction using Bag-of-SURF and Spatial Pyramid Matching methods

In this paper we propose methods to recognize food and estimate calorie using Computer Vision algorithms. Specifically, we propose SURF based bag-of-features and spatial pyramid approaches to recognize the food items. In our experiments, we have achieved upto 86% classification rate on a smaller image dataset of 6 categories. We also experimented with larger PFID dataset containing around 111 food item categories and obtained around 18% classification rate. This trained model can be ported to mobiles platforms such as Android or iOS for realtime recognition purpose.