Food Image Recognition by Using Bag-of-SURF Features and HOG Features

Abstract

Due to food culture, religion, allergy and food intolerance we have to find a good system to help us recognize our food. In this paper, we propose methods to recognize food and to show the ingredients using a bag-of-features (BoF) based on SURF detection features. We also propose bag of SURF features and bag-of HOG Features at the same time with the SURF feature detection to recognize the food items. In the experiment, we have achieved up to 72% of accuracy rate on a small food image dataset of 10 categories. Our experiments show that the proposed representation is significantly accurate at identifying food in the existing methods. Moreover, the enhancement of the visual dataset with more images will improve the accuracy rates, especially for the classes with high diversity.