A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

[1]  Lei Yang,et al.  PFID: Pittsburgh fast-food image dataset , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[2]  Keiji Yanai,et al.  A food image recognition system with Multiple Kernel Learning , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[3]  Aesha Drozdowski,et al.  Standards of medical care in diabetes. , 2004, Diabetes care.

[4]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Arnold W. M. Smeulders,et al.  Color Invariance , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[8]  J. Edge,et al.  Children and adolescents on intensive insulin therapy maintain postprandial glycaemic control without precise carbohydrate counting , 2009, Diabetic medicine : a journal of the British Diabetic Association.

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Jindong Tan,et al.  DietCam: Automatic dietary assessment with mobile camera phones , 2012, Pervasive Mob. Comput..

[11]  Oliver Amftand.  Recognition of dietary activity events using on-body sensors , 2008 .

[12]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[13]  Anil K. Jain,et al.  Content-based hierarchical classification of vacation images , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[14]  Zhiwei Zhu,et al.  Recognition and volume estimation of food intake using a mobile device , 2009, 2009 Workshop on Applications of Computer Vision (WACV).

[15]  Luc Van Gool,et al.  Moment invariants for recognition under changing viewpoint and illumination , 2004, Comput. Vis. Image Underst..

[16]  M. R. Graff,et al.  How well are individuals on intensive insulin therapy counting carbohydrates , 2000 .

[17]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[18]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[19]  P. McElduff,et al.  In children using intensive insulin therapy, a 20‐g variation in carbohydrate amount significantly impacts on postprandial glycaemia , 2012, Diabetic medicine : a journal of the British Diabetic Association.

[20]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[21]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[22]  Kazufumi Kaneda,et al.  Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features , 2016, ArXiv.

[23]  Ana M. González,et al.  Natural conjugate gradient training of multilayer perceptrons , 2006, Neurocomputing.

[24]  Martin Szummer,et al.  Indoor-outdoor image classification , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[25]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[26]  Cordelia Schmid,et al.  Good Practice in Large-Scale Learning for Image Classification , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  D. Maahs,et al.  The Carbohydrate Counting in Adolescents With Type 1 Diabetes (CCAT) Study , 2009 .

[28]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[29]  J. Edge,et al.  Can children with Type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks? , 2009, Diabetic medicine : a journal of the British Diabetic Association.

[30]  Daniel P. Siewiorek,et al.  Wearable context-aware food recognition for calorie monitoring , 2008, 2008 12th IEEE International Symposium on Wearable Computers.

[31]  Stefano Soatto,et al.  Localizing Objects with Smart Dictionaries , 2008, ECCV.

[32]  David Kerr,et al.  Diabetes Interactive Diary: A New Telemedicine System Enabling Flexible Diet and Insulin Therapy While Improving Quality of Life , 2009, Diabetes Care.

[33]  David S. Ebert,et al.  The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation , 2010, IEEE Journal of Selected Topics in Signal Processing.

[34]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[35]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[36]  V. Basevi.  Standards of Medical Care in Diabetes—2010 , 2010, Diabetes Care.