Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets
暂无分享,去创建一个
Huiru Zheng | Raymond Bond | Anne Moorhead | Patrick McAllister | Huiru Zheng | Patrick McAllister | R. Bond | A. Moorhead
[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.