[1] Abdel-Hakim, A.E., & Farag, A.A. (2006). CSIFT: A SIFT descriptor with color invariant characteristics. Computer Vision and Pattern Recognition, 2, 1978–1983. 
[2] Agrawal, M., Konolige, K., & Blas, M.R. (2008). CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching. Lecture Notes in Computer Science, 5305, 102–115. 
[3] Ahonen, T., Hadid, A., & Pietikäinen, M. (2006). Face description with local binary patterns: application to face recognition. Transactions on Pattern Analysis and Machine Intelligence, 28, 2037–2041. 
[4] Ahuja, J., Montville, J.B., Omolewa-Tomobi, G., Heendeniya, K.Y., Martin, C.L., Steinfeldt, L.C., Anand, J., Adler, M.E., LaComb, R.P., & Moshfegh, A.J. (2012). USDA food and nutrient database for dietary studies, 5.0–documentation and user guide. US Department of Agriculture, Agricultural Research Service, Food Surveys Research Group: Beltsville, MD, USA. 
[5] Aizawa, K., Silva, G.C., Ogawa, M., & Sato, Y. (2010). Food Log by snapping and processing images. 2010 16th International Conference on Virtual Systems and Multimedia, 71-74. 
[6] Akpro Hippocrate, E. A., Suwa, H., Arakawa, Y., & Yasumoto, K. (2016). Food weight estimation using smartphone and cutlery. In: Proceedings of the First Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems (IoT of Health ‘16). Association for Computing Machinery, New York, NY, USA, 9–14. 
[7] Allegra, D., Anthimopoulos, M., Dehais, J., Lu, Y., Stanco, F., Farinella, G.M., & Mougiakakou, S. (2017). A multimedia database for automatic meal assessment systems. Lecture Notes in Computer Science, 10590, 471-478. 
[8] Allegra, D., Erba, D., Farinella, G.M., Grazioso, G., Maci, P.D., Stanco, F., & Tomaselli, V. (2019). Learning to rank food images. Lecture Notes in Computer Science, 11752, pp. 629–639. 
[9] Battiato, S., Farinella, G.M., Gallo, G., & Ravì, D. (2010). Exploiting textons distributions on spatial hierarchy for scene classification. Journal on Image and Video Processing, 2010, 919367. 
[10] Battiato, S., Farinella, G.M., Puglisi, G., & Ravì, D. (2014). Aligning codebooks for near duplicate image detection. Multimedia Tools and Applications, 72, 1483–1506. 
[11] Bay, H., Tuytelaars, T., & Van Gool, L. (2006). SURF: Speeded Up Robust Features. Lecture Notes in Computer Science, 3951, 404–417. 
[12] Belongie, S., Alik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. Transactions on Pattern Analysis and Machine Intelligence, 24, 509–522. 
[13] Bettadapura, V., Thomaz, E., Parnami, A., Abowd, G.D., & Essa, I. (2015). leveraging context to support automated food recognition in restaurants. IEEE Winter Conference on Applications of Computer Vision, 580–587. 
[14] Bosch, M., Schap, T., Zhu, F., Khanna, N., Boushey, C.J., Delp, E.J. (2011). Integrated database system for mobile dietary assessment and analysis. International Conference on Multimedia and Expo, 1–6. 
[15] Bossard, L., Guillaumin, M., & Van Gool, L. (2014). Food-101 – Mining discriminative components with random forests. Lecture Notes in Computer Science, 8694, 446–461. 
[16] Brosnan, T., & Sun, D.W. (2004). Improving quality inspection of food products by computer vision - A review. Journal of Food Engineering, 61, 3–16. 
[17] Buemi, F., Massa, M., & Sandini, G. (1995). Agrobot: a robotic system for greenhouse operations. Workshop on Robotics in Agriculture and the Food Industry, pp. 172–184. 
[18] Burghouts, G.J., & Geusebroek, J.M. (2009). Performance evaluation of local colour invariants. Computer Vision and Image Understanding, 113, 48–62. 
[19] Cardenas-Weber, M., Hetzroni, A., & Miles, G.E. (1991). Machine vision to locate melons and guide robotic harvesting. American Society of Agricultural Engineers, p. 21. 
[20] Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., & Yang, J. (2009). PFID: Pittsburgh fast-food image dataset. International Conference on Image Processing, pp. 289–292. 
[21] Chen, M.Y., Yang, Y.H., Ho, C.J., Wang, S.H., Liu, S.M., Chang, E., Yeh, C.H., & Ouhyoung, M. (2012). Automatic Chinese food identification and quantity estimation. SIGGRAPH Asia 2012 Technical Briefs, 1–4. 
[22] Chen, N., Lee, Y. Y., Rabb, M., & Schatz, B. (2010). Toward dietary assessment via mobile phone video cameras. AMIA . Annual Symposium proceedings. pp. 106–110. 
[23] Dalal, N., & Triggs, B. Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, 1, pp. 886-893. 
[24] Dehais, J., Shevchik, S., Diem, P., & Mougiakakou, S.G. (2013). Food volume computation for self dietary assessment applications. International Conference on Bioinformatics and Bioengineering. 
[25] Delwiche, J.F. (2012). You eat with your eyes first. Physiology & Behavior, 107, 502–504. 
[26] Deng, Y., & Manjunath, B.S. (2001). Unsupervised segmentation of color-texture regions in images and video. Transactions on Pattern Analysis and Machine Intelligence, 23, 800–810. 
[27] Donadello, I., Dragoni, M. (2019). Ontology-driven food category classification in images. International Conference on Image Analysis and Processing. pp. 607–617. 
[28] Du, C.J., & Sun, D.W. (2006). Learning techniques used in computer vision for food quality evaluation: a review. Journal of Food Engineering, 72, 39–55. 
[29] Du, C.J.; & Sun, D.W. (2008). Multi-classification of pizza using computer vision and support vector machine. Journal of Food Engineering, 86, 232–242. 
[30] Fang, S., Liu, C., Zhu, F., Delp, E.J., & Boushey, C.J. (2015). Single-view food portion estimation based on geometric models. 2015 IEEE International Symposium on Multimedia (ISM). pp. 385–390. 
[31] Farinella, G.M., Allegra, D., Moltisanti, M., Stanco, F., & Battiato, S. (2016). Retrieval and classification of food images. Computers in Biology and Medicine, 77, 23–39. 
[32] Farinella, G.M., Allegra, D., & Stanco, F. (2015). A benchmark dataset to study the representation of food images. Lecture Notes in Computer Science, 8927, pp. 584–599. 
[33] Farinella, G.M., Allegra, D., & Stanco, F. (2015). On the exploitation of one class classification to distinguish food vs non-food images. Lecture Notes in Computer Science, 9281, 375–383. 
[34] Farinella, G.M., Moltisanti, M., & Battiato, S. (2014). classifying food images represented as bag of textons. International Conference on Image Processing. pp. 5212–5216. 
[35] Farinella, G.M., Moltisanti, M., & Battiato, S. (2015). Food recognition using consensus vocabularies. Lecture Notes in Computer Science, 9281, 384–392. doi:10.1007/978-3-31923222-5_47.
[36] Felzenszwalb, P.F., Girshick, R.B., Mcallester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part-based models. Transactions on Pattern Analysis and Machine Intelligence, 32, 1627–1645. 
[37] Fischler, M.A., & Bolles, R.C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24, 381–395. 
[38] Fontanellaz, M., Christodoulidis, S., & Mougiakakou, S. (2019). Self-attention and ingredient-attention based model for recipe retrieval from image queries. International Workshop on Multimedia Assisted Dietary Management. pp. 25–31. 
[39] Foroni, F., Pergola, G., Argiris, G., & Rumiati, R.I. (2013). The FoodCast research image database (FRIDa). Frontiers in Human Neuroscience, 7. 
[40] Gunasekaran, S. (1996). Computer vision technology for food quality assurance. Trends in Food Science & Technology, 7, 245–256. 
[41] Hammond, R.A., Levine, R. (2010). The economic impact of obesity in the United States. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 3, 285–295. 
[42] Herranz, L., Ruihan, X., & Shuqiang, J. (2015). A probabilistic model for food image recognition in restaurants. International Conference on Multimedia and Expo. pp. 1–6. 
[43] Ho, T.K. (1995). Random decision forests. International Conference on Document Analysis and Recognition, 1, pp. 278–282. 
[44] Hoashi, H., Joutou, T., & Yanai, K. (2010). Image recognition of 85 food categories by feature fusion. International Symposium on Multimedia, pp. 296–301. 
[45] Hu, Y., Cheng, X., Chia, L.T., Xie, X., Rajan, D., & Tan, A.H. (2009). Coherent phrase model for efficient image near-duplicate retrieval. Transactions on Multimedia, 11, 1434–1445. 
[46] Kawano, Y., & Yanai, K. (2014). Automatic expansion of a food image dataset leveraging existing categories with domain adaptation. Lecture Notes in Computer Science, 8927, pp. 3–17. 
[47] Kawano, Y., & Yanai, K. (2014). Food image recognition with deep convolutional features. International Joint Conference on Pervasive and Ubiquitous Computing. pp. 589–593. 
[48] Kawano, Y., & Yanai, K. (2013). Real-time mobile food recognition system. 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 1–7. 
[49] Kiliç, K., Boyaci, I.H., Köksel, H., & Küsmenoglu, I. (2007). A classification system for beans using computer vision system and artificial neural networks. Journal of Food Engineering, 78, 897–904. 
[50] Killgore, W.D., & Yurgelun-Todd, D.A. (2005). Body mass predicts orbitofrontal activity during visual presentations of high-calorie foods. Neuroreport, 16, 859–863. 
[51] Kitamura, K., De Silva, C., Yamasaki, T., & Aizawa, K. (2010). Image processing based approach to food balance analysis for personal food logging. International Conference on Multimedia and Expo. pp. 625–630. 
[52] Kitamura, K., Yamasaki, T., & Aizawa, K. (2008). Food log by analyzing food images. International Conference on Multimedia. pp. 999–1000. 
[53] Kitamura, K., Yamasaki, T., & Aizawa, K. (2009). FoodLog: capture, analysis and retrieval of personal food images via web. Workshop on Multimedia for cooking and eating activities. pp. 23–30. 
[54] Kitamura, K., Yamasaki, T., & Aizawa, K. (2010). Personalization of food image analysis. International Conference on Virtual Systems and Multimedia. pp. 75–78. 
[55] Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21, 1–6. 
[56] Kong, F., & Tan, J. (2012). DietCam: Automatic dietary assessment with mobile camera phones. Pervasive and Mobile Computing, 8, 147–163. 
[57] Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Neural Information Processing Systems. pp. 1097–1105. 
[58] Lazebnik, S., Schmid, C., & Ponce, J. (2005). A sparse texture representation using local affine regions. Transactions on Pattern Analysis and Machine Intelligence, 27, 1265–1278. 
[59] Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. Conference onComputer Vision and Pattern Recognition, 2, pp. 2169–2178. 
[60] Levi, P., Falla, A., & Pappalardo, R. (1988). Image controlled robotics applied to citrus fruit harvesting. International Conference on Robot Vision and Sensory Controls. pp. 2–4. 
[61] Liu, J., Johns, E., Atallah, L., Pettitt, C., Lo, B., Frost, G., & Yang, G.Z. (2012). An intelligent food-intake monitoring system using wearable sensors. International Conference on Wearable and Implantable Body Sensor Networks. 
[62] Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91–110. 
[63] Lu, Y., Allegra, D., Anthimopoulos, M., Stanco, F., Farinella, G.M., & Mougiakakou, S. (2018). A Multi-task learning approach for meal assessment. Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management. pp. 46–52. 
[64] Lu, Y., Stathopoulou, T., Vasiloglou, M.F., Christodoulidis, S., Blum, B., Walser, T., Meier, V., Stanga, Z., & Mougiakakou, S. (2019). an artificial intelligence-based system for nutrient intake assessment of hospitalised patients. International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 5696–5699. 
[65] Marĉelja, S. (1980). Mathematical description of the responses of simple cortical cells. Journal of the Optical Society of America, 70, 1297–1300. 
[66] Marin, J., Biswas, A., Ofli, F., Hynes, N., Salvador, A., Aytar, Y., Weber, I., & Torralba, A. (2019). Recipe1M+: A dataset for learning cross-modal embeddings for cooking recipes and food Images. IEEE transactions on pattern analysis and machine intelligence. 
[67] Maruyama, Y., De Silva, G.C., Yamasaki, T., & Aizawa, K. (2010). Personalization of food image analysis. International Conference on Virtual Systems and Multimedia. pp. 75–78. 
[68] Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004). Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing, 22, 761–767. 
[69] Medic, N., Ziauddeen, H., Forwood, S.E., Davies, K.M., Ahern, A.L., Jebb, S.A., Marteau, T.M., & Fletcher, P.C. (2016). The presence of real food usurps hypothetical health value judgment in overweight people. eNeuro, 3, 0025–16. 
[70] Meyers, A., Johnston, N., Rathod, V., Korattikara, A., Gorban, A., Silberman, N., Guadarrama, S., Papandreou, G., Huang, J., & Murphy, K.P. (2015). Im2Calories: towards an automated mobile vision food diary. Available from: https://static.googleusercontent.com/media/research.google.co m/it//pubs/archive/44321.pdf. 
[71] Min, W., Jiang, S., Liu, L., Rui, Y., & Jain, R. (2019). A survey on food computing. ACM Computing Surveys (CSUR), 52, 92. 
[72] Munkevik, P., Duckett, T., & Hall, G. (2007). A computer vision system for appearance-based descriptive sensory evaluation of meals. Journal of Food Engineering, 78, 246–256. 
[73] Munkevik, P., Duckett, T., & Hall, G. (2004). Vision system learning for ready meal characterisation. International Conference on Engineering and Food. 
[74] Nguyen, D.T., Zong, Z., Ogunbona, P., & Li, W. (2010). Object detection using non-redundant local binary patterns. International Conference on Image Processing. pp. 4609–4612. 
[75] Nishida, C., Uauy, R., Kumanyika, S., & Shetty, P. (2004). The joint WHO/FAO expert consultation on diet, nutrition and the prevention of chronic diseases: process, product and policy implications. Public health nutrition, 7(1a), 245-250. 
[76] Nistér, D., & Stewénius, H. Scalable recognition with a vocabulary tree. (2006). Computer Vision and Pattern Recognition, 2, 2161–2168. 
[77] Ortis, A., Farinella, G. M., & Battiato, S. (2020). Survey on visual sentiment analysis. IET Image Processing, 14(8), 1440-1456. 
[78] Ortis, A., Farinella, G.M., & Battiato, S. (2019). Predicting social image popularity dynamics at time zero. IEEE Access, 1–1. 
[79] Ortis, A., Farinella, G.M., Torrisi, G., & Battiato, S. (2020). Exploiting objective text description of images for visual sentiment analysis. Multimedia Tools and Applications. 
[80] Parrish, E.A., & Goksel, K.A. (1977). Pictorial pattern recognition applied to fruit harvesting. Transactions of the American Society of Agricultural and Biological Engineers, 20, 822–827. 
[81] Perronnin, F., & Dance, C. (2007). Fisher Kernels on visual vocabularies for image categorization. Computer Vision and Pattern Recognition, pp. 1–8. 
[82] Perronnin, F., Sánchez, J., & Mensink, T. (2010). Improving the Fisher Kernel for large-scale image classification. Lecture Notes in Computer Science, 6314, pp. 143–156. 
[83] Petit, O., Cheok, A.D., Oullier, O. (2016). Can food porn make us slim? how brains of consumers react to food in digital environments. Integrative Food, Nutrition and Metabolism, 3, 251–255. 
[84] Pham, C., Jackson, D., Schöning, J., Bartindale, T., Plotz, T., & Olivier, P. (2013). FoodBoard: surface contact imaging for food recognition. International Joint Conference on Pervasive and Ubiquitous Computing. pp. 749–752. 
[85] Plebe, A., & Grasso, G. (2019). The unbearable shallow understanding of deep learning. Minds and Machines, 29(4), 515-553. 
[86] Pouladzadeh, P., Yassine, A., & Shirmohammadi, S. (2015). FooDD: Food Detection Dataset for Calorie Measurement Using Food Images. Lecture Notes in Computer Science, 9281, 441–448. 
[87] Puri, M., Zhu, Z., Yu, Q., Divakaran, A., & Sawhney, H. (2009). Recognition and volume estimation of food intake using a mobile device. Workshop on Applications of Computer Vision. 
[88] Qi, X., Xiao, R., Li, C.G., Qiao, Y., Guo, J., & Tang, X. (2014). Pairwise rotation invariant co-occurrence local binary pattern. Transactions on Pattern Analysis and Machine Intelligence, 36, 2199–2213. 
[89] Ragusa, F., Tomaselli, V., Furnari, A., Battiato, S., & Farinella, G.M. (2016). Food vs non-food classification. International Workshop on Multimedia Assisted Dietary Management. pp. 77–81. 
[90] Rahmana, M.H., Pickering, M.R., Kerr, D., Boushey, C.J., & Delp, E.J. (2012). a new texture feature for improved food recognition accuracy in a mobile phone based dietary assessment system. International Conference on Multimedia and Expo Workshops. pp. 418–423. 
[91] Ravì, D., Lo, B., & Yang, G.Z. (2015). Real-time food intake classification and energy expenditure estimation on a mobile device. International Conference on Wearable and Implantable Body Sensor Networks. 
[92] Rich, A.J. (1981). A programmable calculator system for the estimation of nutritional intake of hospital patients. The American Journal of Clinical Nutrition, 34, 2276–2279. 
[93] Rosenbaum, M., Sy, M., Pavlovich, K., Leibel, R.L., & Hirsch, J. (2008). Leptin reverses weight loss–induced changes in regional neural activity responses to visual food stimuli. The Journal of Clinical Investigation, 118, 2583–2591. 
[94] Ruihan, X., Herranz, L., Shuqiang, J., Shuang, W., Xinhang, S., & Jain, R. (2015). Geolocalized modeling for dish recognition. Transactions on Multimedia, 17, 1187–1199. 
[95] Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013). Image classification with the fisher vector: theory and practice. International Journal of Computer Vision, 105, 222–245. 
[96] Shotton, J., Johnson, M., Cipolla, R. (2013). Semantic texton forests for image categorization and segmentation. Conference in Advances in Computer Vision and Pattern Recognition. pp. 211–227. 
[97] Shroff, G., Smailagic, A., & Siewiorek, D. P. (2008). Wearable context-aware food recognition for calorie monitoring. 12th IEEE International Symposium on Wearable Computers. pp. 119-120. IEEE. 
[98] Slaughter, D.C, & Harrell, R.C. (1989). Discriminating fruit for robotic harvest using color in natural outdoor scenes. Transactions of the American Society of Agricultural and Biological Engineers, 32, 757–763. 
[99] Sun, D.W. (2000). Inspecting pizza topping percentage and distribution by a computer vision method. Journal of Food Engineering, 44, 245–249. 
[100] Sun, M., Burke, L.E., Mao, Z.H., Chen, Y., Chen, H.C., Bai, Y., Li, Y., Li, C., & Jia, W. (2014). eButton: a wearable computer for health monitoring and personal assistance. Proceedings of the 51st Annual Design Automation Conference. ACM. pp. 1–6. 
[101] Suthumchai, N., Thongsukh, S., Yusuksataporn, P., & Tangsripairoj, S. (2016). FoodForCare: An Android application for self-care with healthy food. International Student Project Conference (ICT-ISPC). pp. 89–92. 
[102] Tola, E., Lepetit, V., & Fua, P. (2009). DAISY: An efficient dense descriptor applied to wide-baseline stereo. Transactions on Pattern Analysis and Machine Intelligence, 32, 815–830. 
[103] Topchy, A., Jain, A.K., & Punch, W. (2005). Clustering ensembles: models of consensus and weak partitions. Transactions on Pattern Analysis and Machine Intelligence, 27, 1866–1881. 
[104] Varma, M., & Ray, D. (2007). Learning The Discriminative Power-Invariance Trade-Off. International Conference on Computer Vision. pp. 1–8. 
[105] Varma, M., & Zisserman, A. (2005). A Statistical Approach to Texture Classification from Single Images. International Journal of Computer Vision, 62, 61–81. 
[106] Vedaldi, A., & Zisserman, A. (2012). Efficient additive kernels via explicit feature maps. Transactions on Pattern Analysis and Machine Intelligence, 34, 480–492. 
[107] Wenz, A., Jäckle, A., & Couper, M.P. (2019). Willingness to use mobile technologies for data collection in a probability household panel. Survey Research Methods, 13, 1–22. 
[108] Wright, P.D., Shearing, G., Rich, A.J., & Johnston, I. (1978) The role of a computer in
[108] Wright, P.D., Shearing, G., Rich, A.J., & Johnston, I. (1978) The role of a computer in the management of clinical parenteral nutrition. Journal of Parenteral and Enteral Nutrition, 2, 652–657. 
[109] Wu, W., & Yang, J. (2009). Food recognition using statistics of pairwise local features. International Conference on Multimedia and Expo. pp. 1210–1213. 
[110] Xin, W., Kumar, D., Thome, N., Cord, M., & Precioso, F. (2015). Recipe recognition with large multimodal food dataset. International Conference on Multimedia Expo Workshops. pp. 1–6. 
[111] Xu, K., Zhou, R., Takei, K., & Hong, M. (2019). Toward Flexible Surface-Enhanced Raman Scattering (SERS) Sensors for Point-of-Care Diagnostics. Advanced Science, 6. 
[112] Xu, M.L., Gao, Y., Han, X.X. & Zhao, B. (2017). detection of pesticide residues in food using surface-enhanced raman spectrometry: a review. Journal of Agricultural and Food Chemistry, 65, 6719–6726. 
[113] Yanai, K., & Joutou, T. (2009). SURF: Speeded Up Robust Features. International Conference on Image Processing. pp. 285–288. 
[114] Yanai, K., & Kawano, Y. (2015). Food image recognition using deep convolutional network with pre-training and fine-tuning. International Conference on Multimedia & Expo Workshops. pp. 1–6. 
[115] Yang, S., Chen, M., Pomerleau, D., & Sukthankar, R. (2010). Food recognition using statistics of pairwise local features. Conference on Computer Vision and Pattern Recognition. pp. 2249–2256. 
[116] Zhang, W., Yu, Q., Siddiquie, B., Divakaran, A., & Sawhney, H. (2015). “Snap-n-Eat” food recognition and nutrition estimation on a smartphone. Journal of diabetes science and technology, 9, 525–533. 
[117] Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C.J., Ebert, D.S., & Delp, E.J. (2010). The use of mobile devices in aiding dietary assessment and evaluation. European Journal of Clinical Nutrition, 4, 756–766. 
[118] Zong, Z., Nguyen, D.T., Ogunbona, P., & Li, W. (2010). On the combination of local texture and global structure for food classification. International Symposium on Multimedia. pp. 204–211.