A Modified All-and-One Classification Algorithm Combined with the Bag-of-Features Model to Address the Food Recognition Task

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Dietary intake monitoring can play an important role in reducing the risk of diet related chronic diseases. Automatic systems that support patients to count the nutrient contents, like carbohydrates (CHO), of their meals, can provide valuable tools. In this study, a food recognition system is proposed, which consists of two modules performing feature extraction and classification of food images, respectively. The dataset used consists of 1200 food images split into six categories (bread, meat, potatoes, rice, pasta and vegetables). Speeded Up Robust Features (SURF) along with Color and Local Binary Pattern (LBP) features are extracted from the food images. The Bag-Of-Features (BOF) model is used in order to reduce the features space. A modified version of the All-And-One Support Vector Machine (SVM) is proposed to perform the task of classification and its performance is evaluated against several classifiers that follow the SVM or the K-Nearest Neighbours (KNN) approach. The proposed classification method has achieved the highest levels of accuracy (Acc = 94.2 %) in comparison with all the other classifiers.