Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets

[1]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[2]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[3]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

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

[6]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[7]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[8]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[9]  R. Pontius,et al.  Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment , 2011 .

[10]  Alexei A. Efros,et al.  Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[13]  S. Riedel-Heller,et al.  Economic costs of overweight and obesity. , 2013, Best practice & research. Clinical endocrinology & metabolism.

[14]  Edward J. Delp,et al.  Food image analysis: Segmentation, identification and weight estimation , 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

[15]  Marios Anthimopoulos,et al.  A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model , 2014, IEEE Journal of Biomedical and Health Informatics.

[16]  Giovanni Maria Farinella,et al.  A Benchmark Dataset to Study the Representation of Food Images , 2014, ECCV Workshops.

[17]  Christopher M. Wharton,et al.  Dietary self-monitoring, but not dietary quality, improves with use of smartphone app technology in an 8-week weight loss trial. , 2014, Journal of nutrition education and behavior.

[18]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[19]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Matthieu Guillaumin,et al.  Food-101 - Mining Discriminative Components with Random Forests , 2014, ECCV.

[21]  Keiji Yanai,et al.  Food image recognition with deep convolutional features , 2014, UbiComp Adjunct.

[22]  Keiji Yanai,et al.  FoodCam-256: A Large-scale Real-time Mobile Food RecognitionSystem employing High-Dimensional Features and Compression of Classifier Weights , 2014, ACM Multimedia.

[23]  Keiji Yanai,et al.  Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation , 2014, ECCV Workshops.

[24]  Gian Luca Foresti,et al.  A Structured Committee for Food Recognition , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[25]  Matthieu Cord,et al.  Recipe recognition with large multimodal food dataset , 2015, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[26]  Giovanni Maria Farinella,et al.  Food Recognition Using Consensus Vocabularies , 2015, ICIAP Workshops.

[27]  Paolo Napoletano,et al.  Local Angular Patterns for Color Texture Classification , 2015, ICIAP Workshops.

[28]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[30]  Touradj Ebrahimi,et al.  Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model , 2016, MADiMa @ ACM Multimedia.

[31]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Paolo Napoletano,et al.  Evaluating color texture descriptors under large variations of controlled lighting conditions , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[33]  Jindong Tan,et al.  DietCam: Multiview Food Recognition Using a Multikernel SVM , 2016, IEEE Journal of Biomedical and Health Informatics.

[34]  Giovanni Maria Farinella,et al.  Food vs Non-Food Classification , 2016, MADiMa @ ACM Multimedia.

[35]  Monica Mordonini,et al.  Food Image Recognition Using Very Deep Convolutional Networks , 2016, MADiMa @ ACM Multimedia.

[36]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[37]  Tran Quoc Bao,et al.  Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants , 2016, The Lancet.

[38]  Vinod Vokkarane,et al.  DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment , 2016, ICOST.

[39]  Edward Sazonov,et al.  Feature Extraction Using Deep Learning for Food Type Recognition , 2017, IWBBIO.

[40]  Petia Radeva,et al.  Exploring Food Detection Using CNNs , 2017, EUROCAST.

[41]  P. Ciampolini,et al.  Automatic diet monitoring: a review of computer vision and wearable sensor-based methods , 2017, International journal of food sciences and nutrition.