Authors:
Kalliopi Dalakleidi
;
Myriam Sarantea
and
Konstantina Nikita
Affiliation:
National Technical University of Athens, Greece
Keyword(s):
Diabetes, All-And-One, Bag-Of-Features, Food Recognition System.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Development of Assistive Technology
;
Distributed and Mobile Software Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition and Machine Learning
;
Software Engineering
;
Symbolic Systems
Abstract:
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.
(More)