M. M. Anthimopoulos, L. Gianola, L. Scarnato, P. Diem, and S. G. Mougiakakou. A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE Journal of Biomedical and Health Informatics, 18(4):1261–1271, 2014. https://doi.org/10.1109/JBHI.2014.2308928. (Crossref)

L. Bossard, M. Guillaumin, and L. Van Gool. Food-101 – Mining discriminative components with random forests. In Proc. 13th European Conf. Computer Vision ECCV 2014, Part VI, volume 8694 of Lecture Notes in Computer Science, pages 446–461, Zurich, Switzerland, Sep 6-12, 2014. https://doi.org/10.1007/978-3-319-10599-4_29. (Crossref)

L. Breiman. Random forests. Machine Learning, 45(1):5–32, 2001. https://doi.org/10.1023/A:1010933404324. (Crossref)

M. C. Carter, V. J. Burley, C. Nykjaer, and J. E. Cade. Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. Journal of Medical Internet Research, 15(4):e32, 2013. https://doi.org/10.2196/jmir.2283. (Crossref)

M. Chen, K. Dhingra, W. Wu, L. Yang, R. Sukthankar, and J. Yang. PFID: Pittsburgh fast-food image dataset. In Proc. 16th IEEE Int. Conf. Image Processing ICIP 2009, pages 289–292, Cairo, Egypt, Nov 7-10, 2009. https://doi.org/10.1109/ICIP.2009.5413511. (Crossref)

M.-Y. Chen, Y.-H. Yang, C.-J. Ho, et al. Automatic Chinese food identification and quantity estimation. In SIGGRAPH Asia SA 2012 Technical Briefs, pages 29:1–29:4, Singapore, Singapore, Nov 28-Dec 1, 2012. ACM. https://doi.org/10.1145/2407746.2407775. (Crossref)

S. Christodoulidis, M. Anthimopoulos, and S. Mougiakakou. Food recognition for dietary assessment using deep convolutional neural networks. In Proc. New Trends in Image Analysis and Processing – ICIAP 2015 Workshops, volume 9281 of Lecture Notes in Computer Science, pages 458–465, Genoa, Italy, Sep 7-8, 2015. https://doi.org/10.1007/978-3-319-23222-5_56. (Crossref)

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision SLCV 2004, European Conference on Computer Vision ECCV 2004, pages 1–22, Prague, Czech Republic, May 15, 2004. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.72.604.

G. M. Farinella, D. Allegra, and F. Stanco. A benchmark dataset to study the representation of food images. In Proc. European Conf. Computer Vision ECCV 2014 Workshops, Part III, pages 584–599, Zurich, Switzerland, Sep 6-7 and 12, 2014 (published in 2015). https://doi.org/10.1007/978-3-319-16199-0_41. (Crossref)

G. M. Farinella, M. Moltisanti, and S. Battiato. Classifying food images represented as Bag of Textons. In Proc. IEEE Int. Conf. Image Processing ICIP 2014, pages 5212–5216, Paris, France, Oct 27-30, 2014. https://doi.org/10.1109/ICIP.2014.7026055. (Crossref)

J. Garcia, N. Martinel, A. Gardel, I. Bravo, G. L. Foresti, and C. Micheloni. Discriminant context information analysis for post-ranking person re-identification. IEEE Transactions on Image Processing, 26(4):1650–1665, 2017. https://doi.org/10.1109/TIP.2017.2652725. (Crossref)

T.S. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In Proc. 11th Int. Conf. Neural Information Processing Systems, pages 487–493, Devner, USA, Dec 1-3, 1999. http://dl.acm.org/citation.cfm?id=3009055.3009124.

H. Jegou, M. Douze, C. Schmid, and P. Perez. Aggregating local descriptors into a compact image representation. In Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition CVPR 2010, pages 3304–3311, San Francisco, USA, Jun 13-18, 2010. https://doi.org/10.1109/CVPR.2010.5540039. (Crossref)

H. Kagaya, K. Aizawa, and M. Ogawa. Food detection and recognition using convolutional neural network. In Proc. 22Nd ACM Int. Conf. Multimedia MM 2014, pages 1085–1088, New York, NY, USA, Nov 3-7, 2014. ACM. https://doi.org/10.1145/2647868.2654970. (Crossref)

Y. Kawano and K. Yanai. Real-time mobile food recognition system. In Proc. IEEE Conf. Computer Vision and Pattern Recognition CVPR 2013 Workshops, pages 1–7, Portland, USA, Jun 23-28, 2013. https://doi.org/10.1109/CVPRW.2013.5. (Crossref)

Y. Kawano and K. Yanai. Food image recognition with deep convolutional features. In Proc. 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, UbiComp 2014 Adjunct, pages 589–593, New York, NY, USA, 2014. ACM. https://doi.org/10.1145/2638728.2641339. (Crossref)

Y. Kawano and K. Yanai. Foodcam: A real-time food recognition system on a smartphone. Multimedia Tools and Applications, 74(14):5263–5287, 2015. https://doi.org/10.1007/s11042-014-2000-8. (Crossref)

F. Kong and J. Tan. DietCam: Automatic dietary assessment with mobile camera phones. Pervasive and Mobile Computing, 8(1):147–163, 2012. https://doi.org/10.1016/j.pmcj.2011.07.003. (Crossref)

K. I. Laws. Rapid texture identification. In Proc. 24th Ann. Tech. Symp. Image Processing for Missile Guidance, volume 0238 of Proc. SPIE, pages 376–380, San Diego, USA, Jul 29-Aug 1, 1980. https://doi.org/10.1117/12.959169. (Crossref)

T. Leung and J. Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43(1):29–44, 2001. https://doi.org/10.1023/A:1011126920638. (Crossref)

N. Martinel and C. Micheloni. Sparse matching of random patches for person re-identification. In Proc. Int. Conf. Distributed Smart Cameras ICDSC 2014, pages 8:1–8:6, Venezia Mestre, Italy, Nov 4-7, 2014. https://doi.org/10.1145/2659021.2659034. (Crossref)

N. Martinel, C. Micheloni, and G. L. Foresti. Robust painting recognition and registration for mobile augmented reality. IEEE Signal Processing Letters, 20(11):1022–1025, 2013. https://doi.org/10.1109/LSP.2013.2279014. (Crossref)

N. Martinel, C. Micheloni, and C. Piciarelli. Learning pairwise feature dissimilarities for person re-identification. In Proc. 7th Int. Conf. Distributed Smart Cameras ICDSC 2013, pages 1–6, Hong Kong, China, Oct 29-Nov 1, 2013. https://doi.org/10.1109/ICDSC.2013.6778209. (Crossref)

N. Martinel, C. Piciarelli, G.L. Foresti, and C. Micheloni. Mobile Food Recognition with an Extreme Deep Tree. In Proc. Int. Conf. Distributed Smart Cameras, pages 56–61, Paris, France, 2016. https://doi.org/10.1145/2967413.2967428. (Crossref)

N. Martinel, C. Piciarelli, and C. Micheloni. A supervised extreme learning committee for food recognition. Computer Vision and Image Understanding, 148:67–86, 2016. https://doi.org/10.1016/j.cviu.2016.01.012. (Crossref)

N. Martinel, C. Piciarelli, C. Micheloni, and G. L. Foresti. On filter banks of texture features for mobile food classification. In Proc. 9th Int. Conf. Distributed Smart Cameras ICDSC 2015, pages 14–19, Seville, Spain, Sep 8-11, 2015. ACM. https://doi.org/10.1145/2789116.2789132. (Crossref)

N. Martinel, C. Piciarelli, C. Micheloni, and G. L. Foresti. A structured committee for food recognition. In Proc. IEEE Int. Conf. Computer Vision Workshop ICCVW 2015, pages 484–492, Santiago, Chile, Dec 9-13, 2015. https://doi.org/10.1109/ICCVW.2015.70. (Crossref)

Niki Martinel, Gian Luca Foresti, and Christian Micheloni. Wide-Slice Residual Networks for Food Recognition. In Proc. Winter Conference on Applications of Computer Vision, 2018. https://doi.org/10.1109/WACV.2018.00068 (Crossref)

Y. Matsuda, H. Hoashi, and K. Yanai. Recognition of multiple-food images by detecting candidate regions. In Proc. Int. Conf. Multimedia and Expo, pages 25–30, Melbourne, Australia, Jul 9-13, 2012. https://doi.org/10.1109/ICME.2012.157. (Crossref)

Y. Matsuda and K. Yanai. Multiple-food recognition considering co-occurrence employing manifold ranking. In Proc. 21st Int. Conf. Pattern Recognition ICPR 2012, pages 2017–2020, Tsukuba, Japan, Nov 11-15, 2013. https://ieeexplore.ieee.org/document/6460555.

F. Perronnin, J. S´anchez, and T. Mensink. Improving the Fisher kernel for large-scale image classification. In Proc. European Conf. Computer Vision ECCV 2010, volume 6314 of Lecture Notes in Computer Science, pages 143–156, Heraklion, Crete, Greece, Sep 5-11, 2010. https://doi.org/10.1007/978-3-642-15561-1 11. (Crossref)

M. Puri, Z. Zhu, Q. Yu, A. Divakaran, and H. Sawhney. Recognition and volume estimation of food intake using a mobile device. In Proc. Workshop on Applications of Computer Vision WACV 2019, pages 1–8, Dec 7-8, 2009. https://doi.org/10.1109/WACV.2009.5403087. (Crossref)

X. Qi, R. Xiao, C. Li, Y. Qiao, J. Guo, and X. Tang. Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans. Pattern Analysis and Machine Intelligence, 36(11):2199–2213, 2014. https://doi.org/10.1109/TPAMI.2014.2316826. (Crossref)

J. Sanchez, F. Perronnin, T. Mensink, and J. Verbeek. Image classification with the Fisher vector: Theory and practice. International Journal of Computer Vision, 105(3):222–245, 2013. https://doi.org/10.1007/s11263-013-0636-x. (Crossref)

C. Schmid. Constructing models for content-based image retrieval. In Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition CVPR 2001, volume 2, pages II-39–II-45, Kauai, Hawaii, USA, Dec 8-14, 2001. https://doi.org/10.1109/CVPR.2001.990922. (Crossref)

M. Varma and A. Zisserman. Texture classification: are filter banks necessary? In Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition CVPR 2003, volume 2, pages II-691–II-698, Madison, USA, Jun 18-20, 2003. https://doi.org/10.1109/CVPR.2003.1211534. (Crossref)

M. Vernier, N. Martinel, C. Micheloni, and G. L. Foresti. Remote feature learning for mobile re-identification. In Proc. 7th Int. Conf. Distributed Smart Cameras ICDSC 2013, pages 1–6, Hong Kong, China, Oct 29-Nov 1, 2013. https://doi.org/10.1109/ICDSC.2013.6778221. (Crossref)

T.P. Weldon, W.E. Higgins, and D.F. Dunn. Efficient Gabor filter design for texture segmentation. Pattern Recognition, 29(12):2005–2015, 1996. https://doi.org/10.1016/S0031-3203(96)00047-7. (Crossref)

World Health Organization. Obesity and overweight – fact sheet n. 311, 2015. http://www.who.int/mediacentre/factsheets/fs311/en/.

K. Yanai and Y. Kawano. Food image recognition using deep convolutional network with pre-training and fine-tuning. In Proc. IEEE Int. Conf. Multimedia & Expo Workshops ICMEW 2015, pages 1–6, Turin, Italy, Jun 29-Jul 3, 2015. https://doi.org/10.1109/ICMEW.2015.7169816. (Crossref)

S. Yang, M. Chen, D. Pomerleau, and R. Sukthankar. Food recognition using statistics of pairwise local features. In Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition CVPR 2010, pages 2249–2256, San Francisco, USA, Jun 13-18, 2010. https://doi.org/10.1109/CVPR.2010.5539907. (Crossref)

W. Zhang, Q. Yu, B. Siddiquie, A. Divakaran, and H. Sawhney. “Snap-n-Eat”: Food recognition and nutrition estimation on a smartphone. Journal of Diabetes Science and Technology, 9(3):525–533, 2015. https://doi.org/10.1177/1932296815582222. (Crossref)

F. Zhu, M. Bosch, I. Woo, et al. The use of mobile devices in aiding dietary assessment and evaluation. IEEE Journal of Selected Topics in Signal Processing, 4(4):756–766, 2010. https://doi.org/10.1109/JSTSP.2010.2051471. (Crossref)