Weiqing Min
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Abstract
Food is essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding human behavior, improving human health, and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from different sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food-related issues in medicine, biology, gastronomy, and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, a series of works has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews that shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this article, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.
- Sofiane Abbar, Yelena Mejova, and Ingmar Weber. 2015. You tweet what you eat: Studying food consumption through Twitter. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 3197--3206. Google Scholar
Digital Library
- Palakorn Achananuparp, Ee Peng Lim, and Vibhanshu Abhishek. 2018. Does journaling encourage healthier choices? Analyzing healthy eating behaviors of food journalers. In Proceedings of the International Digital Health Conference. Google ScholarDigital Library
- Eduardo Aguilar, Marc Bolaños, and Petia Radeva. 2017a. Exploring food detection using CNNs. In Proceedings of the International Conference on Computer Aided Systems Theory. 339--347.Google Scholar
- Eduardo Aguilar, Marc Bolaños, and Petia Radeva. 2017b. Food recognition using fusion of classifiers based on CNNs. In Proceedings of the International Conference on Image Analysis and Processing. 213--224.Google ScholarCross Ref
- Eduardo Aguilar, Marc Bolaños, and Petia Radeva. 2019. Regularized uncertainty-based multi-task learning model for food analysis. J. Vis. Comm. Image Rep. 60 (2019), 360--370.Google ScholarCross Ref
- E. Aguilar, B. Remeseiro, M. Bolaños, and P. Radeva. 2018. Grab, pay and eat: Semantic food detection for smart restaurants. IEEE Trans. Multimed. 20, 12 (2018), 3266--3275.Google ScholarDigital Library
- Yong-Yeol Ahn, Sebastian E. Ahnert, James P. Bagrow, and Albert-László Barabási. 2011. Flavor network and the principles of food pairing. Sci. Rep. 1, 7377 (2011), 196.Google Scholar
- K. Aizawa, K. Maeda, M. Ogawa, Y. Sato, M. Kasamatsu, K. Waki, and H. Takimoto. 2014. Comparative study of the routine daily usability of FoodLog: A smartphone-based food recording tool assisted by image retrieval. J. Diab. Sci. Technol. 8, 2 (2014), 203--208.Google ScholarCross Ref
- Kiyoharu Aizawa, Yuto Maruyama, He Li, and Chamin Morikawa. 2013. Food balance estimation by using personal dietary tendencies in a multimedia food log. IEEE Trans. Multimed. 15, 8 (2013), 2176--2185. Google ScholarDigital Library
- Kiyoharu Aizawa and Makoto Ogawa. 2015. FoodLog: Multimedia tool for healthcare applications. IEEE MultiMedia 22, 2 (2015), 4--8.Google ScholarDigital Library
- M. M. Anthimopoulos, L. Gianola, L. Scarnato, P. Diem, and S. G. Mougiakakou. 2014. A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE J. Biomed. Health Inform. 18, 4 (2014), 1261--1271.Google ScholarCross Ref
- Masashi Anzawa, Sosuke Amano, Yoko Yamakata, Keiko Motonaga, Akiko Kamei, and Kiyoharu Aizawa. 2019. Recognition of multiple food items in a single photo for use in a buffet-style restaurant. IEICE Transactions 102-D, 2 (2019), 410--414.Google Scholar
- Shuang Ao and Charles X. Ling. 2015. Adapting new categories for food recognition with deep representation. In Proceedings of the IEEE International Conference on Data Mining Workshop. 1196--1203. Google ScholarDigital Library
- Gianni Barlacchi, Azad Abad, Emanuele Rossinelli, and Alessandro Moschitti. 2016. Appetitoso: A search engine for restaurant retrieval based on dishes. In Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it. 46--50.Google ScholarCross Ref
- Roberto Camacho Barranco, Laura M. Rodriguez, Rebecca Urbina, and M. Shahriar Hossain. 2016. Is a picture worth ten thousand words in a review dataset? Retrieved from: arXiv:1606.07496 (2016).Google Scholar
- Oscar Beijbom, Neel Joshi, Dan Morris, Scott Saponas, and Siddharth Khullar. 2015. Menu-match: Restaurant-specific food logging from images. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision. 844--851. Google ScholarDigital Library
- D. Bell. 1997. Consuming geographies: We are where we eat. Routledge.Google Scholar
- Vinay Bettadapura, Edison Thomaz, Aman Parnami, Gregory D. Abowd, and Irfan Essa. 2015. Leveraging context to support automated food recognition in restaurants. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision. 580--587. Google ScholarDigital Library
- Marc Bolaños, Aina Ferrà, and Petia Radeva. 2017. Food ingredients recognition through multi-label learning. In International Conference on Image Analysis and Processing. 394--402.Google ScholarCross Ref
- Marc Bolaños and Petia Radeva. 2017. Simultaneous food localization and recognition. In Proceedings of the International Conference on Pattern Recognition. 3140--3145.Google Scholar
- Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 1247--1250. Google ScholarDigital Library
- Ruud M. Bolle, Jonathan H. Connell, Norman Haas, Rakesh Mohan, and Gabriel Taubin. 1996. VeggieVision: A produce recognition system. In Proceedings of the IEEE Workshop on Applications of Computer Vision. 244--251. Google ScholarDigital Library
- M. Bosch, F. Zhu, N. Khanna, C. J. Boushey, and E. J. Delp. 2011. Combining global and local features for food identification in dietary assessments. In Proceedings of the IEEE International Conference on Image Processing. 1789--1792.Google Scholar
- Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. 2014. Food-101-mining discriminative components with random forests. In Proceedings of the European Conference on Computer Vision. 446--461.Google ScholarCross Ref
- Tamara Bucher, Klazine Van der Horst, and Michael Siegrist. 2013. Fruit for dessert. How people compose healthier meals. Appetite 60 (2013), 74--80.Google ScholarCross Ref
- B. V. R. Silva and J. Cui. 2017. A survey on automated food monitoring and dietary management systems. J. Health Med. Inform. 8, 3 (2017).Google Scholar
- L. Canetti, E. Bachar, and E. M. Berry. 2002. Food and emotion. Behav. Proc. 60, 2 (2002), 157--164.Google ScholarCross Ref
- Daniel Capurro, Kate Cole, Maria I. Echavarría, Jonathan Joe, Tina Neogi, and Anne M. Turner. 2014. The use of social networking sites for public health practice and research: A systematic review. J. Med. Internet Res. 16, 3 (2014), e79.Google ScholarCross Ref
- Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, and Matthieu Cord. 2018. Cross-modal retrieval in the cooking context: Learning semantic text-image embeddings. In Proceedings of the 41st International ACM SIGIR Conference on Research Development in Information Retrieval. 35--44. Google ScholarDigital Library
- Minsuk Chang, Leonore V. Guillain, Hyeungshik Jung, Vivian M. Hare, Juho Kim, and Maneesh Agrawala. 2018. RecipeScape: An interactive tool for analyzing cooking instructions at scale. In Proceedings of the Conference on Human Factors in Computing Systems. 451:1--451:12. Google ScholarDigital Library
- Itthi Chatnuntawech, Kittipong Tantisantisom, Paisan Khanchaitit, Thitikorn Boonkoom, Berkin Bilgic, and Ekapol Chuangsuwanich. 2018. Rice classification using spatio-spectral deep convolutional neural network. Retrieved from: arXiv:1805.11491 (2018).Google Scholar
- Hao Chen, Jianglong Xu, Guangyi Xiao, Qi Wu, Shiqin Zhang, Hao Chen, Jianglong Xu, Guangyi Xiao, Qi Wu, and Shiqin Zhang. 2018. Fast auto-clean CNN model for online prediction of food materials. J. Parallel Distrib. Comput. 117 (2018), 218--227.Google ScholarDigital Library
- Jingjing Chen, Chong-Wah Ngo, and Tat-Seng Chua. 2017a. Cross-modal recipe retrieval with rich food attributes. In Proceedings of the ACM on Multimedia Conference. 1771--1779. Google ScholarDigital Library
- Jingjing Chen and Chong-Wah Ngo. 2016. Deep-based ingredient recognition for cooking recipe retrieval. In Proceedings of the ACM on Multimedia Conference. 32--41. Google ScholarDigital Library
- Jingjing Chen, Lei Pang, and Chong Wah Ngo. 2017b. Cross-modal recipe retrieval: How to cook this dish? In Proceedings of the International Conference on Multimedia Modeling. 588--600.Google ScholarCross Ref
- Mei Chen, Kapil Dhingra, Wen Wu, Lei Yang, Rahul Sukthankar, and Jie Yang. 2009. PFID: Pittsburgh fast-food image dataset. In Proceedings of the IEEE International Conference on Image Processing. 289--292. Google ScholarDigital Library
- Mei Yun Chen, Yung Hsiang Yang, Chia Ju Ho, Shih Han Wang, Shane Ming Liu, Eugene Chang, Che Hua Yeh, and Ouhyoung Ming. 2012. Automatic Chinese food identification and quantity estimation. In Proceedings of the SIGGRAPH Asia 2012 Technical Briefs. 29. Google ScholarDigital Library
- Steven W. Chen, Shreyas S. Skandan, Sandeep Dcunha, Jnaneshwar Das, Edidiong Okon, Chao Qu, Camillo Jose Taylor, and Vijay Kumar. 2017c. Counting apples and oranges with deep learning: A data driven approach. IEEE Robot. Automat. Lett. 2, 2 (2017), 781--788.Google ScholarCross Ref
- Xin Chen, Hua Zhou, and Liang Diao. 2017e. ChineseFoodNet: A large-scale image dataset for Chinese food recognition. Retrieved from: CoRR abs/1705.02743 (2017).Google Scholar
- Zikuan Chen and Yang Tao. 2001. Food safety inspection using “from presence to classification” object-detection model. Pattern Recog. 34, 12 (2001), 2331--2338. Google ScholarDigital Library
- Hao Cheng, Markus Rokicki, and Eelco Herder. 2017. The influence of city size on dietary choices and food recommendation. In Proceedings of the Conference on User Modeling, Adaptation and Personalization. 359--360. Google ScholarDigital Library
- Stergios Christodoulidis, Marios Anthimopoulos, and Stavroula Mougiakakou. 2015. Food recognition for dietary assessment using deep convolutional neural networks. In Proceedings of the International Conference on Image Analysis and Processing. 458--465.Google ScholarCross Ref
- J. Chung, J. Chung, W. Oh, Y. Yoo, W. G. Lee, and H. Bang. 2017. A glasses-type wearable device for monitoring the patterns of food intake and facial activity. Sci. Rep. 7 (2017), 41690.Google Scholar
- Gianluigi Ciocca, Paolo Napoletano, and Raimondo Schettini. 2015. Food recognition and leftover estimation for daily diet monitoring. In Proceedings of the International Conference on Image Analysis and Processing. 334--341.Google ScholarCross Ref
- Gianluigi Ciocca, Paolo Napoletano, and Raimondo Schettini. 2016. Food recognition: A new dataset, experiments, and results. IEEE J. Biomed. Health Inform. 21, 3 (2016), 588--598.Google ScholarCross Ref
- Gianluigi Ciocca, Paolo Napoletano, and Raimondo Schettini. 2017. Learning CNN-based features for retrieval of food images. In Proceedings of the International Conference on Image Analysis and Processing. 426--434.Google ScholarCross Ref
- Gianluigi Ciocca, Paolo Napoletano, and Raimondo Schettini. 2018. CNN-based features for retrieval and classification of food images. Comput. Vis. Image Underst. 176--177 (2018), 70--77.Google Scholar
- Felicia Cordeiro, Elizabeth Bales, Erin Cherry, and James Fogarty. 2015a. Rethinking the mobile food journal: Exploring opportunities for lightweight photo-based capture. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 3207--3216. Google ScholarDigital Library
- F. Cordeiro, D. A. Epstein, E. Thomaz, E. Bales, A. K. Jagannathan, G. D. Abowd, and J. Fogarty. 2015b. Barriers and negative nudges: Exploring challenges in food journaling. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 1159--1162. Google ScholarDigital Library
- Aron Culotta. 2014. Estimating county health statistics with Twitter. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 1335--1344. Google ScholarDigital Library
- Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. 2018. Scaling egocentric vision: The EPIC-KITCHENS dataset. In Proceedings of the European Conference on Computer Vision. 753--771.Google Scholar
- Joachim Dehais, Marios Anthimopoulos, Sergey Shevchik, and Stavroula Mougiakakou. 2017. Two-view 3D reconstruction for food volume estimation. IEEE Trans. Multimed. 19, 5 (2017), 1090--1099. Google ScholarDigital Library
- Gregory Druck. 2013. Recipe attribute prediction using review text as supervision. In Proceedings of the International Joint Conference on Artificial Intelligence Workshop on Cooking with Computers.Google Scholar
- Takumi Ege and Keiji Yanai. 2017. Simultaneous estimation of food categories and calories with multi-task CNN. In Proceedings of the 15th IAPR International Conference on Machine Vision Applications. 198--201.Google ScholarCross Ref
- Mehdi Elahi, David Elsweiler, Georg Groh, Morgan Harvey, Bernd Ludwig, Francesco Ricci, and Alan Said. 2017. User nutrition modelling and recommendation: Balancing simplicity and complexity. In Proceedings of the Conference on User Modeling, Adaptation and Personalization. 93--96. Google ScholarDigital Library
- David Elsweiler, Morgan Harvey, and Morgan Harvey. 2017. Exploiting food choice biases for healthier recipe recommendation. In International ACM SIGIR Conference on Research and Development in Information Retrieval. 575--584. Google ScholarDigital Library
- Shaobo Fang, Zeman Shao, Runyu Mao, Chichen Fu, Deborah A. Kerr, Carol J. Boushey, Edward J. Delp, and Fengqing Zhu. 2018. Single-view food portion estimation: Learning image-to-energy mappings using generative adversarial networks. In Proceedings of the IEEE International Conference on Image Processing. 251--255.Google ScholarCross Ref
- G. M. Farinella, D. Allegra, M. Moltisanti, F. Stanco, and S. Battiato. 2016. Retrieval and classification of food images. Comput. Biol. Med. 77 (2016), 23--39. Google ScholarDigital Library
- Giovanni Maria Farinella, Dario Allegra, and Filippo Stanco. 2014a. A benchmark dataset to study the representation of food images. In Proceedings of the European Conference on Computer Vision. 584--599.Google Scholar
- Giovanni Maria Farinella, Dario Allegra, Filippo Stanco, and Sebastiano Battiato. 2015a. On the exploitation of one class classification to distinguish food vs non-food images. In Proceedings of the International Conference on Image Analysis and Processing. 375--383.Google ScholarCross Ref
- Giovanni Maria Farinella, Marco Moltisanti, and Sebastiano Battiato. 2014b. Classifying food images represented as bag of textons. In Proceedings of the IEEE International Conference on Image Processing. 5212--5216.Google ScholarCross Ref
- Giovanni Maria Farinella, Marco Moltisanti, and Sebastiano Battiato. 2015b. Food recognition using consensus vocabularies. In Proceedings of the International Conference on Image Analysis and Processing. 384--392.Google ScholarCross Ref
- Aleksandr Farseev and Tat-Seng Chua. 2017. Tweet can be fit: Integrating data from wearable sensors and multiple social networks for wellness profile learning. ACM Trans. Inform. Syst. 35, 4 (2017), 42:1--42:34. Google ScholarDigital Library
- Jianlong Fu, Heliang Zheng, and Tao Mei. 2017. Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4476--4484.Google ScholarCross Ref
- Mouzhi Ge, Mehdi Elahi, Ignacio Fernández-Tobías, Francesco Ricci, and David Massimo. 2015. Using tags and latent factors in a food recommender system. In Proceedings of the International Conference on Digital Health. 105--112. Google ScholarDigital Library
- Andrea Giampiccoli and Janet Hayward Kalis. 2012. Tourism, food, and culture: Community-based tourism, local food, and community development in Mpondoland. Cult. Agricult. 34, 2 (2012), 101--123.Google ScholarCross Ref
- S. A. Golder and M. W. Macy. 2011. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 6051 (2011), 1878--1881.Google ScholarCross Ref
- C. M. Hall and C. M. Hall. 2003. Wine, Food, and Tourism Marketing. Routledge.Google Scholar
- Jun Harashima, Yuichiro Someya, and Yohei Kikuta. 2017. Cookpad image dataset: An image collection as infrastructure for food research. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 1229--1232. Google ScholarDigital Library
- Caleb Harper and Mario Siller. 2015. OpenAG: A globally distributed network of food computing. IEEE Pervas. Comput. 14, 4 (2015), 24--27.Google ScholarDigital Library
- Marvin Harris. 1985. Good to eat: Riddles of food and culture. Amer. Anthrop. 2 (1985).Google Scholar
- Morgan Harvey, Bernd Ludwig, and David Elsweiler. 2013. You are what you eat: Learning user tastes for rating prediction. In Proceedings of the International Symposium on String Processing and Information Retrieval. 153--164. Google ScholarDigital Library
- Hamid Hassannejad, Guido Matrella, Paolo Ciampolini, Ilaria De Munari, Monica Mordonini, and Stefano Cagnoni. 2016. Food image recognition using very deep convolutional networks. In Proceedings of the International Workshop on Multimedia Assisted Dietary Management. 41--49. Google ScholarDigital Library
- Hongsheng He, Fanyu Kong, and Jindong Tan. 2017. DietCam: Multiview food recognition using a multikernel SVM. IEEE J. Biomed. Health Inform. 20, 3 (2017), 848--855.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.Google ScholarCross Ref
- Jose Luis Hernandez-Hernandez, Mario Hernandez-Hernandez, Severino Feliciano-Morales, Valentín Álvarez-Hilario, and Israel Herrera-Miranda. 2017. Search for optimum color space for the recognition of oranges in agricultural fields. In Proceedings of the International Conference on Technologies and Innovation. 296--307.Google ScholarCross Ref
- Luis Herranz, Shuqiang Jiang, and Ruihan Xu. 2017. Modeling restaurant context for food recognition. IEEE Trans. Multimed. 19, 2 (2017), 430--440. Google ScholarDigital Library
- Luis Herranz, Ruihan Xu, and Shuqiang Jiang. 2015. A probabilistic model for food image recognition in restaurants. In Proceedings of the IEEE International Conference on Multimedia and Expo. 1--6.Google ScholarCross Ref
- P. Herruzo, M.Bolaños, and P. Radeva. 2016. Can a CNN recognize Catalan diet? In Proceedings of the American Institute of Physics Conference Series. 211--252.Google Scholar
- Hajime Hoashi, Taichi Joutou, and Keiji Yanai. 2010. Image recognition of 85 food categories by feature fusion. In Proceedings of the IEEE International Symposium on Multimedia. 296--301. Google ScholarDigital Library
- S. Horiguchi, S. Amano, M. Ogawa, and K. Aizawa. 2018. Personalized classifier for food image recognition. IEEE Trans. Multimed. 20, 10 (2018), 2836--2848.Google ScholarCross Ref
- Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. Retrieved from: CoRR abs/1704.04861 (2017).Google Scholar
- G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2261--2269.Google Scholar
- Ramesh Jain. 2015. Let’s weave the visual web. IEEE Multimedia 22, 3 (2015), 66--72.Google ScholarDigital Library
- Jermsak Jermsurawong and Nizar Habash. 2015. Predicting the structure of cooking recipes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 781--786.Google ScholarCross Ref
- A. R. Jimenez, A. K. Jain, R. Ceres, and J. L. Pons. 1999. Automatic fruit recognition: A survey and new results using range/attenuation images. Pattern Recog. 32, 10 (1999), 1719--1736.Google ScholarCross Ref
- Justin Johnson, Ranjay Krishna, Michael Stark, Li Jia Li, David A. Shamma, Michael S. Bernstein, and Fei Fei Li. 2015. Image retrieval using scene graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3668--3678.Google ScholarCross Ref
- Taichi Joutou and Keiji Yanai. 2010. A food image recognition system with multiple kernel learning. In Proceedings of the IEEE International Conference on Image Processing. 285--288. Google ScholarDigital Library
- Hokuto Kagaya and Kiyoharu Aizawa. 2015. Highly accurate food/non-food image classification based on a deep convolutional neural network. In Proceedings of the International Conference on Image Analysis and Processing. 350--357.Google ScholarCross Ref
- Hokuto Kagaya, Kiyoharu Aizawa, and Makoto Ogawa. 2014. Food detection and recognition using convolutional neural network. In Proceedings of the ACM International Conference on Multimedia. 1085--1088. Google ScholarDigital Library
- Payam Karisani and Eugene Agichtein. 2018. Did you really just have a heart attack? Towards robust detection of personal health mentions in social media. Retrieved from: CoRR abs/1802.09130 (2018). Google ScholarDigital Library
- Ravi Karkar, Jessica Schroeder, Daniel A. Epstein, Laura R. Pina, Jeffrey Scofield, James Fogarty, Julie A. Kientz, Sean A. Munson, Roger Vilardaga, and Jasmine Zia. 2017. TummyTrials: A feasibility study of using self-experimentation to detect individualized food triggers. In Proceedings of the Conference on Human Factors in Computing Systems. 6850--6863. Google ScholarDigital Library
- Parneet Kaur, Karan Sikka, and Ajay Divakaran. 2017. Combining weakly and webly supervised learning for classifying food images. Retrieved from: abs/1712.08730 (2017).Google Scholar
- Yoshiyuki Kawano and Keiji Yanai. 2014a. Automatic expansion of a food image dataset leveraging existing categories with domain adaptation. In Proceedings of the European Conference on Computer Vision. 3--17.Google Scholar
- Yoshiyuki Kawano and Keiji Yanai. 2014b. Food image recognition with deep convolutional features. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 589--593. Google ScholarDigital Library
- Yoshiyuki Kawano and Keiji Yanai. 2014c. Foodcam-256: A large-scale real-time mobile food recognition system employing high-dimensional features and compression of classifier weights. In Proceedings of the ACM International Conference on Multimedia. 761--762. Google ScholarDigital Library
- Yoshiyuki Kawano and Keiji Yanai. 2015. FoodCam: A real-time food recognition system on a smartphone. Multimed. Tools Applic. 74, 14 (2015), 5263--5287. Google ScholarDigital Library
- Sunil K. Khanna. 2009. Food and culture: A reader (2nd Ed.). Ecol. Food Nutrit. 48, 2 (2009), 157--159.Google ScholarCross Ref
- W. D. Killgore, A. D. Young, L. A. Femia, P. Bogorodzki, J. Rogowska, and D. A. Yurgeluntodd. 2003. Cortical and limbic activation during viewing of high- versus low-calorie foods. Neuroimage 19, 4 (2003), 1381--1394.Google ScholarCross Ref
- W. D. Killgore and D. A. Yurgelun-Todd. 2005. Body mass predicts orbitofrontal activity during visual presentations of high-calorie foods. Neuroreport 16, 8 (2005), 859--863.Google ScholarCross Ref
- Kyung Joong Kim and Chang Ho Chung. 2016. Tell me what you eat, and I will tell you where you come from: A data science approach for global recipe data on the web. IEEE Access 4 (2016), 8199--8211.Google ScholarCross Ref
- Osame Kinouchi, Rosa W. Diezgarcia, Adriano J. Holanda, Pedro Zambianchi, and Antonio C. Roque. 2008. The nonequilibrium nature of culinary evolution. New J. Phys. 10, 7 (2008), 073020.Google ScholarCross Ref
- Keigo Kitamura, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2008. Food log by analyzing food images. In Proceedings of the ACM International Conference on Multimedia. 999--1000. Google ScholarDigital Library
- Keigo Kitamura, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2009. FoodLog: Capture, analysis and retrieval of personal food images via web. In Proceedings of the ACM Multimedia Workshop on Multimedia for Cooking and Eating Activities. 23--30. Google ScholarDigital Library
- Simon Knez and Luka Šajn. 2015. Food object recognition using a mobile device: State of the art. In Proceedings of the International Conference on Image Analysis and Processing. 366--374.Google ScholarCross Ref
- Ryosuke Kojima, Osamu Sugiyama, and Kazuhiro Nakadai. 2015. Audio-visual scene understanding utilizing text information for a cooking support robot. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 4210--4215.Google ScholarCross Ref
- G. Kolata. 1982. Food affects human behavior. Science 218, 4578 (1982), 1209--10.Google Scholar
- Fanyu Kong and Jindong Tan. 2011. DietCam: Regular shape food recognition with a camera phone. In Proceedings of the International Conference on Body Sensor Networks. 127--132. Google ScholarDigital Library
- Fanyu Kong and Jindong Tan. 2012. DietCam: Automatic dietary assessment with mobile camera phones. Pervas. Mobile Comput. 8, 1 (2012), 147--163. Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the International Conference on Neural Information Processing Systems. 1097--1105. Google ScholarDigital Library
- H. Kuehne, A. Arslan, and T. Serre. 2014. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 780--787. Google ScholarDigital Library
- Tomasz Kusmierczyk and Kjetil Norvag. 2016. Online food recipe title semantics: Combining nutrient facts and topics. In Proceedings of the ACM International on Conference on Information and Knowledge Management. 2013--2016. Google ScholarDigital Library
- Tomasz Kusmierczyk and Christoph Trattner. 2015. Temporal patterns in online food innovation. In Proceedings of the International Conference on World Wide Web. 1345--1350. Google ScholarDigital Library
- Yann LeCun, Yoshua Bengio, and Geoffrey E. Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444.Google Scholar
- Cheng-Yuan Li, Yen-Chang Chen, Wei-Ju Chen, Polly Huang, and Hao-hua Chu. 2013. Sensor-embedded teeth for oral activity recognition. In Proceedings of the International Symposium on Wearable Computers. 41--44. Google ScholarDigital Library
- Huan Chung Li and Wei Min Ko. 2007. Automated food ontology construction mechanism for diabetes diet care. In Proceedings of the International Conference on Machine Learning and Cybernetics. 2953--2958.Google ScholarCross Ref
- Yanchao Liang and Jianhua Li. 2017. Computer vision-based food calorie estimation: Dataset, method, and experiment. Retrieved from: arXiv preprint arXiv:1705.07632 (2017).Google Scholar
- Chang Liu, Yu Cao, Yan Luo, Guanling Chen, Vinod Vokkarane, and Yunsheng Ma. 2016. Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment. In Proceedings of the International Conference on Smart Homes and Health Telematics. 37--48. Google ScholarDigital Library
- David G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 2 (2004), 91--110. Google ScholarDigital Library
- Cewu Lu, Ranjay Krishna, Michael Bernstein, and Li Fei-Fei. 2016. Visual relationship detection with language priors. In Proceedings of the European Conference on Computer Vision. 852--869.Google ScholarCross Ref
- Yuzhen Lu, Yuping Huang, and Renfu Lu. 2017. Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: A review. Appl. Sci. 7, 2 (2017), 189.Google ScholarCross Ref
- Chao Ma, Ze Song, Xuhui Sun, and Guangchuan Zhao. 2015. Will low-income populations love spicy foods more? Accounting for tastes. University Library of Munich, Germany.Google Scholar
- Rokicki Markus, Trattner Christoph, and Herder Eelco. 2018. The impact of recipe features, social cues and demographics on estimating the healthiness of online recipes. In Proceedings of the International AAAI Conference on Weblogs and Social Media.Google Scholar
- C. K. Martin, J. B. Correa, H. Han, H. R. Allen, J. C. Rood, C. M. Champagne, B. K. Gunturk, and G. A. Bray. 2012. Validity of the remote food photography method (RFPM) for estimating energy and nutrient intake in near real-time. Obesity 20, 4 (2012), 891--899.Google ScholarCross Ref
- Niki Martinel, Gian Luca Foresti, and Christian Micheloni. 2018. Wide-slice residual networks for food recognition. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision. 567--576.Google ScholarCross Ref
- Niki Martinel, Claudio Piciarelli, and Christian Micheloni. 2016. A supervised extreme learning committee for food recognition. Comput. Vis. Image Underst. 148 (2016), 67--86. Google ScholarDigital Library
- Niki Martinel, Claudio Piciarelli, Christian Micheloni, and Gian Luca Foresti. 2015. A structured committee for food recognition. In Proceedings of the IEEE International Conference on Computer Vision Workshop. 484--492. Google ScholarDigital Library
- Takuma Maruyama, Yoshiyuki Kawano, and Keiji Yanai. 2012. Real-time mobile recipe recommendation system using food ingredient recognition. In Proceedings of the ACM International Workshop on Interactive Multimedia on Mobile and Portable Devices. 27--34. Google ScholarDigital Library
- Yuji Matsuda, Hajime Hoashi, and Keiji Yanai. 2012. Recognition of multiple-food images by detecting candidate regions. In Proceedings of the IEEE International Conference on Multimedia and Expo. 25--30. Google ScholarDigital Library
- Y. Matsuda and K. Yanai. 2012. Multiple-food recognition considering co-occurrence employing manifold ranking. In Proceedings of the International Conference on Pattern Recognition. 2017--2020.Google Scholar
- Patrick McAllister, Huiru Zheng, Raymond Bond, and Anne Moorhead. 2018. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets. Comput. Biol. Med. 95 (2018), 217--233.Google ScholarDigital Library
- K. McCrickerd and C. G. Forde. 2016. Sensory influences on food intake control: Moving beyond palatability. Obesity Reviews 17, 1 (2016), 18--29.Google ScholarCross Ref
- Yelena Mejova, Sofiane Abbar, and Hamed Haddadi. 2016. Fetishizing food in digital age: #Foodporn around the world. In Proceedings of the International Conference on Weblogs and Social Media. 250--258.Google Scholar
- Yelena Mejova, Hamed Haddadi, Anastasios Noulas, and Ingmar Weber. 2015. # FoodPorn: Obesity patterns in culinary interactions. In Proceedings of the International Conference on Digital Health. 51--58. Google ScholarDigital Library
- Michele Merler, Hui Wu, Rosario Uceda-Sosa, Quoc-Bao Nguyen, and John R. Smith. 2016. Snap, Eat, RepEat: A food recognition engine for dietary logging. In Proceedings of the International Workshop on Multimedia Assisted Dietary Management. 31--40. Google ScholarDigital Library
- Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, and Kevin P. Murphy. 2015. Im2Calories: Towards an automated mobile vision food diary. In Proceedings of the IEEE International Conference on Computer Vision. 1233--1241. Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. CoRR arXiv:1905.04097 (2013).Google Scholar
- Weiqing Min, Bing-Kun Bao, Shuhuan Mei, Yaohui Zhu, Yong Rui, and Shuqiang Jiang. 2018. You are what you eat: Exploring rich recipe information for cross-region food analysis. IEEE Trans. Multimed. 20, 4 (2018), 950--964. Google ScholarDigital Library
- Weiqing Min, Shuqiang Jiang, Jitao Sang, Huayang Wang, Xinda Liu, and Luis Herranz. 2017a. Being a supercook: Joint food attributes and multi-modal content modeling for recipe retrieval and exploration. IEEE Trans. Multimed. 19, 5 (2017), 1100--1113. Google ScholarDigital Library
- Weiqing Min, Shuqiang Jiang, Shuhui Wang, Jitao Sang, and Shuhuan Mei. 2017b. A delicious recipe analysis framework for exploring multi-modal recipes with various attributes. In Proceedings of the ACM on Multimedia Conference. 402--410. Google ScholarDigital Library
- Weiqing Min, Shuqiang Jiang, Shuhui Wang, Ruihan Xu, Yushan Cao, Luis Herranz, and Zhiqiang He. 2017c. A survey on context-aware mobile visual recognition. Multimed. Syst. 23, 6 (2017), 647--665. Google ScholarDigital Library
- Zhao Yan Ming, Jingjing Chen, Yu Cao, Ciaran Forde, Chong Wah Ngo, and Tat Seng Chua. 2018. Food photo recognition for dietary tracking; System and experiment. In Proceedings of the International Conference on Multi Media Modelling. 129--141.Google ScholarCross Ref
- Mingui Sun, John D. Fernstrom, Wenyan Jia, Steven A. Hackworth, Ning Yao, Yuecheng Li, Chengliu Li, Madelyn H. Fernstrom, and Robert J. Sclabassi. 2010. A wearable electronic system for objective dietary assessment. J. Amer. Diet. Assoc. 110, 1 (2010), 45.Google ScholarCross Ref
- Mark Mirtchouk, Christopher Merck, and Samantha Kleinberg. 2016. Automated estimation of food type and amount consumed from body-worn audio and motion sensors. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 451--462. Google ScholarDigital Library
- Ruiko Miyano, Yuko Uematsu, and Hideo Saito. 2012. Food region detection using bag-of-features representation and color feature. In Proceedings of the International Conference on Computer Vision Theory and Applications. 709--713.Google Scholar
- Ole G. Mouritsen, Rachel Edwards-Stuart, Yong-Yeol Ahn, and Sebastian E. Ahnert. 2017. Data-driven methods for the study of food perception, preparation, consumption, and culture. Frontiers in ICT 4 (2017), 15.Google ScholarCross Ref
- Nitish Nag, Vaibhav Pandey, and Ramesh Jain. 2017a. Health multimedia: Lifestyle recommendations based on diverse observations. In Proceedings of the ACM International Conference on Multimedia Retrieval. 99--106. Google ScholarDigital Library
- Nitish Nag, Vaibhav Pandey, and Ramesh Jain. 2017b. Live personalized nutrition recommendation engine. In Proceedings of the International Workshopon Multimedia for Personal Health and Health Care. 61--68. Google ScholarDigital Library
- Medic Nenad, Ziauddeen Hisham, Suzanna E. Forwood, Kirsty M. Davies, Amy L. Ahern, Susan A. Jebb, Theresa M. Marteau, and Paul C. Fletcher. 2016. The presence of real food usurps hypothetical health value judgment in overweight people. Eneuro 3, 2 (2016).Google Scholar
- Nestle, Rosenberg, Bieber, Rogers, Haas, Dwyer, and Sigman-Grant. 1998. Behavioral and social influences on food choice—Discussion. Nutrit. Rev. 56, 5 (1998), S64--S74.Google Scholar
- M. Ng, T. Fleming, M. Robinson, B. Thomson, N. Graetz, C. Margono, E. C. Mullany, S. Biryukov, C. Abbafati, and S. F. Abera. 2014. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980--2013: A systematic analysis for the global burden of disease study 2013. Lancet 384, 9945 (2014), 766.Google Scholar
- Duc Thanh Nguyen, Zhimin Zong, Philip O. Ogunbona, Yasmine Probst, and Wanqing Li. 2014. Food image classification using local appearance and global structural information. Neurocomputing 140 (2014), 242--251.Google ScholarCross Ref
- Nag Nitish, Pandey Vaibhav, Sharma Abhisaar, Lam Jonathan, Wang Runyi, and Jain Ramesh. 2017. Pocket dietitian: Automated healthy dish recommendations by location. In Proceedings of the International Conference on Image Analysis and Processing. 444--452.Google Scholar
- Karin Nordstrom, Christian Coff, Hakan Jönsson, Lennart Nordenfelt, and Ulf Gorman. 2013. Food and health: Individual, cultural, or scientific matters? Genes Nutrit. 8, 4 (2013), 357.Google ScholarCross Ref
- Ferda Ofli, Yusuf Aytar, Ingmar Weber, Raggi Al Hammouri, and Antonio Torralba. 2017. Is Saki #Delicious?: The food perception gap on Instagram and its relation to health. In Proceedings of the International Conference on World Wide Web. 509--518. Google ScholarDigital Library
- Koichi Okamoto and Keiji Yanai. 2016. An automatic calorie estimation system of food images on a smartphone. In Proceedings of the International Workshop on Multimedia Assisted Dietary Management. 63--70. Google ScholarDigital Library
- Luciano Oliveira, Victor Costa, Gustavo Neves, Talmai Oliveira, Eduardo Jorge, and Miguel Lizarraga. 2014. A mobile, lightweight, poll-based food identification system. Pattern Recog. 47, 5 (2014), 1941--1952. Google ScholarDigital Library
- Kaoru Ota, Minh Son Dao, Vasileios Mezaris, and Francesco G. B. De Natale. 2017. Deep learning for mobile multimedia: A survey. ACM Trans. Multimed. Comput., Commun., Applic. 13, 3s (2017), 34:1--34:22. Google ScholarDigital Library
- Jaderick P. Pabico, Alona V. De Grano, and Alan L. Zarsuela. 2015. Neural network classifiers for natural food products. Retrieved from: arXiv preprint arXiv:1507.02346 (2015).Google Scholar
- Lili Pan, Samira Pouyanfar, Hao Chen, Jiaohua Qin, Shu Ching Chen, Lili Pan, Samira Pouyanfar, Hao Chen, Jiaohua Qin, and Shu Ching Chen. 2017. DeepFood: Automatic multi-class classification of food materials using deep learning. In Proceedings of the IEEE International Conference on Collaboration and Internet Computing. 181--189.Google Scholar
- Paritosh Pandey, Akella Deepthi, Bappaditya Mandal, and N. B. Puhan. 2017. FoodNet: Recognizing foods using ensemble of deep networks. IEEE Sig. Proc. Lett. 24, 12 (2017), 1758--1762.Google ScholarCross Ref
- D. Pauly. 1986. A simple method for estimating the food consumption of fish populations from growth data and food conversion experiments. Fish. Bull. 84 (1986), 827--839.Google Scholar
- Sri Vijay Bharat Peddi, Pallavi Kuhad, Abdulsalam Yassine, Parisa Pouladzadeh, Shervin Shirmohammadi, and Ali Asghar Nazari Shirehjini. 2017. An intelligent cloud-based data processing broker for mobile e-health multimedia applications. Fut. Gen. Comput. Syst. 66 (2017), 71--86.Google ScholarCross Ref
- Maiyaporn Phanich, Phathrajarin Pholkul, and Suphakant Phimoltares. 2010. Food recommendation system using clustering analysis for diabetic patients. In Proceedings of the International Conference on Information Science and Applications. 1--8.Google ScholarCross Ref
- Parisa Pouladzadeh, Pallavi Kuhad, Sri Vijay Bharat Peddi, Abdulsalam Yassine, and Shervin Shirmohammadi. 2016a. Food calorie measurement using deep learning neural network. In Proceedings of the Instrumentation and Measurement Technology Conference. 1--6.Google ScholarCross Ref
- Parisa Pouladzadeh and Shervin Shirmohammadi. 2017. Mobile multi-food recognition using deep learning. ACM Trans. Multimed. Comput. Commun. Applic. 13, 3s (2017), 1--21. Google ScholarDigital Library
- Parisa Pouladzadeh, Shervin Shirmohammadi, and Rana Al-Maghrabi. 2014. Measuring calorie and nutrition from food image. IEEE Trans. Instrument. Measur. 63, 8 (2014), 1947--1956.Google ScholarCross Ref
- Parisa Pouladzadeh, Shervin Shirmohammadi, and Abdulsalam Yassine. 2016b. You are what you eat: So measure what you eat! IEEE Instrument. Measur. Mag. 19, 1 (2016), 9--15.Google ScholarCross Ref
- Parisa Pouladzadeh, Abdulsalam Yassine, and Shervin Shirmohammadi. 2015. FooDD: An image-based food detection dataset for calorie measurement. In Proceedings of the International Conference on Multimedia Assisted Dietary Management.Google Scholar
- M. Puri, Zhiwei Zhu, Qian Yu, and A. Divakaran. 2009. Recognition and volume estimation of food intake using a mobile device. In Proceedings of the Workshop on Applications of Computer Vision. 1--8.Google Scholar
- Francesco Ragusa, Valeria Tomaselli, Antonino Furnari, Sebastiano Battiato, and Giovanni M. Farinella. 2016. Food vs non-food classification. In Proceedings of the International Workshop on Multimedia Assisted Dietary Management. 77--81. Google ScholarDigital Library
- Daniele Ravl, Benny Lo, and Guang Zhong Yang. 2015. Real-time food intake classification and energy expenditure estimation on a mobile device. In Proceedings of the IEEE International Conference on Wearable and Implantable Body Sensor Networks. 1--6.Google Scholar
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 779--788.Google ScholarCross Ref
- Jaclyn Rich, Hamed Haddadi, and Timothy M. Hospedales. 2016. Towards bottom-up analysis of social food. In Proceedings of the International Conference on Digital Health Conference. ACM, 111--120. Google ScholarDigital Library
- M. Rohrbach, S. Amin, M. Andriluka, and B. Schiele. 2012. A database for fine grained activity detection of cooking activities. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1194--1201. Google ScholarDigital Library
- M. Rosenbaum, M. Sy, K. Pavlovich, R. L. Leibel, and J. Hirsch. 2008a. Leptin reverses weight loss-induced changes in regional neural activity responses to visual food stimuli. J. Clinic. Investig. 118, 7 (2008), 2583--2591.Google Scholar
- Adam Sadilek, Henry Kautz, Lauren Diprete, Brian Labus, Eric Portman, Jack Teitel, and Vincent Silenzio. 2017. Deploying nEmesis: Preventing foodborne illness by data mining social media. AI Magazine 38, 1 (2017), 37--48.Google ScholarCross Ref
- Sina Sajadmanesh, Sina Jafarzadeh, Seyed Ali Ossia, Hamid R. Rabiee, Hamed Haddadi, Yelena Mejova, Mirco Musolesi, Emiliano De Cristofaro, and Gianluca Stringhini. 2017. Kissing Cuisines: Exploring worldwide culinary habits on the web. In Proceedings of the International World Wide Web Conference. ACM, 1013--1021. Google ScholarDigital Library
- Amaia Salvador, Nicholas Hynes, Yusuf Aytar, Javier Marin, Ferda Ofli, Ingmar Weber, and Antonio Torralba. 2017. Learning cross-modal embeddings for cooking recipes and food images. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 3020--3028.Google ScholarCross Ref
- Satoshi Sanjo and Marie Katsurai. 2017. Recipe popularity prediction with deep visual-semantic fusion. In Proceedings of the ACM Conference on Information and Knowledge Management. 2279--2282. Google ScholarDigital Library
- Giovanni Schiboni and Oliver Amft. 2018. Automatic dietary monitoring using wearable accessories. In Seamless Healthcare Monitoring: Advancements in Wearable, Attachable, and Invisible Devices. Springer International Publishing, 369--412.Google Scholar
- Mike Schuster and Kuldip K. Paliwal. 1997. Bidirectional Recurrent Neural Networks. IEEE Press. 2673--2681. Google ScholarDigital Library
- Yagcioglu Semih, Erdem Aykut, Erdem Erkut, and Ikizler-Cinbis Nazli. 2018. RecipeQA: A challenge dataset for multimodal comprehension of cooking recipes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.Google Scholar
- J. Senthilnath, Akanksha Dokania, Manasa Kandukuri, K. N. Ramesh, Gautham Anand, and S. N. Omkar. 2016. Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV. Biosyst. Eng. 146 (2016), 16--32.Google ScholarCross Ref
- Zagoruyko Sergey and Komodakis Nikos. 2016. Wide residual networks. In Proceedings of the British Machine Vision Conference.Google Scholar
- Paul W. Sherman and Jennifer Billing. 1999. Darwinian gastronomy: Why we use spices. Bioscience 49, 6 (1999), 453--463.Google ScholarCross Ref
- Wataru Shimoda and Keiji Yanai. 2015. CNN-based food image segmentation without pixel-wise annotation. In Proceedings of the International Conference on Image Analysis and Processing. 449--457.Google ScholarCross Ref
- Wataru Shimoda and Keiji Yanai. 2016. Foodness proposal for multiple food detection by training of single food images. In Proceedings of the International Workshop on Multimedia Assisted Dietary Management. 13--21. Google ScholarDigital Library
- Thiago H. Silva, Pedro O. S. de Melo, Jussara Almeida, Mirco Musolesi, and Antonio Loureiro. 2014. You are what you eat (and drink): Identifying cultural boundaries by analyzing food 8 drink habits in foursquare. In Proceedings of the International Conference on Weblogs and Social Media.Google Scholar
- Ashutosh Singla, Lin Yuan, and Touradj Ebrahimi. 2016. Food/non-food image classification and food categorization using pre-trained GoogLeNet model. In Proceedings of the International Workshop on Multimedia Assisted Dietary Management. 3--11. Google ScholarDigital Library
- Lillian Sonnenberg, Emily Gelsomin, Douglas E. Levy, Jason Riis, Susan Barraclough, and Anne N. Thorndike. 2013. A traffic light food labeling intervention increases consumer awareness of health and healthy choices at the point-of-purchase. Prevent. Med. 57, 4 (2013), 253--257.Google ScholarCross Ref
- L. B. Sorensen, P. Moller, A. Flint, M. Martens, and A. Raben. 2003. Effect of sensory perception of foods on appetite and food intake: A review of studies on humans. Int. J. Obesity 27, 10 (2003), 1152.Google ScholarCross Ref
- Charles Spence, Carmel A. Levitan, Maya U. Shankar, and Massimiliano Zampini. 2010. Does food color influence taste and flavor perception in humans? Chemosens. Percept. 3, 1 (2010), 68--84.Google ScholarCross Ref
- Nitish Srivastava and Ruslan Salakhutdinov. 2012. Multimodal learning with deep Boltzmann machines. In Proceedings of the International Conference on Neural Information Processing Systems. 2222--2230. Google ScholarDigital Library
- Sebastian Stein and Stephen J. McKenna. 2013. Combining embedded accelerometers with computer vision for recognizing food preparation activities. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 729--738. Google ScholarDigital Library
- Eliza Strickland. 2018. 3 sensors to track every bite and gulp. IEEE Spectrum 55, 7 (2018), 9--10.Google Scholar
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1--9.Google ScholarCross Ref
- Ryosuke Tanno, Koichi Okamoto, and Keiji Yanai. 2016. DeepFoodCam: A DCNN-based real-time mobile food recognition system. In Proceedings of the International Workshop on Multimedia Assisted Dietary Management. 89--89. Google ScholarDigital Library
- Chun-Yuen Teng, Yu-Ru Lin, and Lada A. Adamic. 2012. Recipe recommendation using ingredient networks. In Proceedings of the ACM Web Science Conference. 298--307. Google ScholarDigital Library
- Chakkrit Termritthikun, Paisarn Muneesawang, and Surachet Kanprachar. 2017. NU-InNet: Thai food image recognition using convolutional neural networks on smartphone. J. Telecommun., Electron. Comput. Eng. 9, 2--6 (2017), 63--67.Google Scholar
- Trung Phan Thanh and Daniel Gatica-Perez. 2017. # Healthy# Fondue# Dinner: Analysis and inference of food and drink consumption patterns on Instagram. In Proceedings of the International Conference on Mobile and Ubiquitous Multimedia. 327--338. Google ScholarDigital Library
- Frances E. Thompson and Amy F. Subar. 2017. Dietary Assessment Methodology. In Nutrition in the Prevention and Treatment of Disease (4th Ed.). Academic Press, 5--48.Google Scholar
- Christoph Trattner and David Elsweiler. 2017a. Food recommender systems: Important contributions, challenges and future research directions. Retrieved from: arXiv preprint arXiv:1711.02760 (2017).Google Scholar
- Christoph Trattner and David Elsweiler. 2017b. Investigating the healthiness of internet-sourced recipes: Implications for meal planning and recommender systems. In Proceedings of the International World Wide Web Conference. 489--498. Google ScholarDigital Library
- Christoph Trattner, Markus Rokicki, and Eelco Herder. 2017. On the relations between cooking interests, hobbies and nutritional values of online recipes: Implications for health-aware recipe recommender systems. In Proceedings of the ACM Conference on User Modeling, Adaptation and Personalization. Google ScholarDigital Library
- Akihiro Tsubakida, Sosuke Amano, Kiyoharu Aizawa, and Makoto Ogawa. 2017. Prediction of individual eating habits using short-term food recording. In Proceedings of the Workshop on Multimedia for Cooking and Eating Activities in conjunction with the International Joint Conference on Artificial Intelligence. 45--48. Google ScholarDigital Library
- J. V. Verhagen and L. Engelen. 2006. The neurocognitive bases of human multimodal food perception: Sensory integration. Neurosci. Biobehav. Rev. 30, 5 (2006), 613--50.Google ScholarCross Ref
- Tri Vu, Feng Lin, Nabil Alshurafa, and Wenyao Xu. 2017. Wearable food intake monitoring technologies: A comprehensive review. Computers 6, 1 (2017), 4.Google ScholarCross Ref
- Claudia Wagner, Philipp Singer, and Markus Strohmaier. 2014. The nature and evolution of online food preferences. EPJ Data Sci. 3, 1 (2014), 38.Google ScholarCross Ref
- Kayo Waki, Kiyoharu Aizawa, Shigeko Kato, Hideo Fujita, Hanae Lee, Haruka Kobayashi, Makoto Ogawa, Keisuke Mouri, Takashi Kadowaki, and Kazuhiko Ohe. 2015. DialBetics with a multimedia food recording tool, FoodLog: Smartphone-based self-management for type 2 diabetes. J. Diab. Sci. Technol. 9, 3 (2015), 534--540.Google ScholarCross Ref
- Huayang Wang, Weiqing Min, Xiangyang Li, and Shuqiang Jiang. 2016. Where and what to eat: Simultaneous restaurant and dish recognition from food image. In Proceedings of the Pacific Rim Conference on Multimedia. 520--528. Google ScholarDigital Library
- Liping Wang, Qing Li, Na Li, Guozhu Dong, and Yu Yang. 2008. Substructure similarity measurement in Chinese recipes. In Proceedings of the ACM International Conference on World Wide Web. 979--988. Google ScholarDigital Library
- Xin Wang, Devinder Kumar, Nicolas Thome, Matthieu Cord, and Frederic Precioso. 2015. Recipe recognition with large multimodal food dataset. In Proceedings of the IEEE International Conference on Multimedia and Expo Workshops. 1--6.Google Scholar
- D. A. Williamson, H. R. Allen, P. D. Martin, A. J. Alfonso, B. Gerald, and A. Hunt. 2003. Comparison of digital photography to weighed and visual estimation of portion sizes. J. Amer. Diet. Assoc. 103, 9 (2003), 1139--1145.Google ScholarCross Ref
- Hui Wu, Michele Merler, Rosario Uceda-Sosa, and John R. Smith. 2016. Learning to make better mistakes: Semantics-aware visual food recognition. In Proceedings of the ACM on Multimedia Conference. 172--176. Google ScholarDigital Library
- Wen Wu and Jie Yang. 2009. Fast food recognition from videos of eating for calorie estimation. In Proceedings of the IEEE International Conference on Multimedia and Expo. 1210--1213. Google ScholarDigital Library
- Rong Xiang, Huanyu Jiang, and Yibin Ying. 2014. Recognition of clustered tomatoes based on binocular stereo vision. Comput. Electron. Agricult. 106 (2014), 75--90. Google ScholarDigital Library
- Haoran Xie, Lijuan Yu, and Qing Li. 2011. A hybrid semantic item model for recipe search by example. In Proceedings of the IEEE International Symposium on Multimedia. 254--259. Google ScholarDigital Library
- Ruihan Xu, Luis Herranz, Shuqiang Jiang, Shuang Wang, Xinhang Song, and Ramesh Jain. 2015. Geolocalized modeling for dish recognition. IEEE Trans. Multimed. 17, 8 (2015), 1187--1199.Google ScholarDigital Library
- Keiji Yanai and Yoshiyuki Kawano. 2015. Food image recognition using deep convolutional network with pre-training and fine-tuning. In Proceedings of the IEEE International Conference on Multimedia and Expo Workshops. 1--6.Google ScholarCross Ref
- Keiji Yanai, Ryosuke Tanno, and Koichi Okamoto. 2016. Efficient mobile implementation of a CNN-based object recognition system. In Proceedings of the 24th ACM International Conference on Multimedia. 362--366. Google ScholarDigital Library
- Gao Yang, Zhang Ning, Honghao Wang, Ding Xiang, Ye Xu, Guanling Chen, and Cao Yu. 2016. iHear food: Eating detection using commodity Bluetooth headsets. In Proceedings of the 1st IEEE International Conference on Connected Health: Applications.Google Scholar
- Longqi Yang, Cheng Kang Hsieh, Hongjian Yang, John P. Pollak, Nicola Dell, Serge Belongie, Curtis Cole, and Deborah Estrin. 2017. Yum-Me: A personalized nutrient-based meal recommender system. ACM Trans. Inform. Syst. 36, 1 (2017), 7. Google ScholarDigital Library
- Shulin Yang, Mei Chen, Dean Pomerleau, and Rahul Sukthankar. 2010. Food recognition using statistics of pairwise local features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2249--2256.Google Scholar
- Ning Yu, Desislava Zhekova, Can Liu, and Sandra Kubler. 2013. Do good recipes need butter? Predicting user ratings of online recipes. In Proceedings of the International Workshop on Cooking with Computers.Google Scholar
- Jiawei Zhang, Limeng Cui, Philip S. Yu, and Yuanhua Lv. 2017a. BL-ECD: Broad learning based enterprise community detection via hierarchical structure fusion. In Proceedings of the ACM Conference on Information and Knowledge Management. 859--868. Google ScholarDigital Library
- Mabel Mengzi Zhang. 2011. Identifying the Cuisine of a Plate of Food. Technical Report. University of California, San Diego.Google Scholar
- Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. 2017b. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. Retrieved from: CoRR abs/1707.01083 (2017).Google Scholar
- Xi-Jin Zhang, Yi-Fan Lu, and Song-Hai Zhang. 2016. Multi-task learning for food identification and analysis with deep convolutional neural networks. J. Comput. Sci. Technol. 31, 3 (2016), 489--500.Google ScholarCross Ref
- Jiannan Zheng, Z. Jane Wang, and Chunsheng Zhu. 2017. Food image recognition via superpixel based low-level and mid-level distance coding for smart home applications. Sustainability 9, 5 (2017), 856.Google ScholarCross Ref
- Feng Zhou and Yuanqing Lin. 2016. Fine-grained image classification by exploring bipartite-graph labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1124--1133.Google ScholarCross Ref
- Fengqing Zhu, Marc Bosch, Insoo Woo, Sung Ye Kim, Carol J. Boushey, David S. Ebert, and Edward J. Delp. 2010. The use of mobile devices in aiding dietary assessment and evaluation. IEEE J. Select. Topics Sig. Proc. 4, 4 (2010), 756.Google ScholarCross Ref
- Yaohui Zhu and Shuqiang Jiang. 2018. Deep structured learning for visual relationship detection. In the Association for the Advance of Artificial Intelligence. 7623--7630.Google Scholar
- Yuke Zhu, Ce Zhang, Christopher Ré, and Li Fei-Fei. 2015. Building a large-scale multimodal knowledge base for visual question answering. Retrieved from: CoRR abs/1507.05670 (2015).Google Scholar
- Yu Xiao Zhu, Junming Huang, Zi Ke Zhang, Qian Ming Zhang, Tao Zhou, and Yong Yeol Ahn. 2013. Geography and similarity of regional cuisines in China. Plos One 8, 11 (2013), e79161.Google ScholarCross Ref
- Zhimin Zong, Duc Thanh Nguyen, Philip Ogunbona, and Wanqing Li. 2010. On the combination of local texture and global structure for food classification. In Proceedings of the IEEE International Symposium on Multimedia. 204--211. Google ScholarDigital Library
Index Terms
- A Survey on Food Computing
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Reviews
William Edward Mihalo
The authors have written an extensive survey of the published literature related to food computing. The survey is about 26 pages long, with an additional ten pages of (about 300) references. The authors note: Food computing mainly utilizes the methods from computer science for food-related study. It involves the acquisition and analysis of food data with different modalities (e.g., food images, food logs, recipe, taste, and smell) from different data sources (e.g., the social network, recipe-sharing websites, and cameras). Such analysis resorts to computer vision, machine learning, data mining, and other advanced technologies to connect food and humans. (p. 92:3) The authors cover databases that include recipes, dish images, cooking videos, food attributes, food logs, restaurant-relevant food information, healthiness, and other miscellaneous food data. One challenge of this field is that it is rapidly changing. Many of the database references are no longer available. Some of the databases may require a login and password, and some of the databases may require proprietary software. One problem that has to be solved in this area concerns the detection and analysis of irregularly shaped images. When food is served at a restaurant, its presentation results in an irregularly shaped object. The workaround for this is to have the consumer provide the name of the dish along with its picture. One of the goals of food computing is to provide consumers with a summary of sources for food information. Suppose a consumer goes to a restaurant and orders a serving of spaghetti and meatballs. In this situation, the consumer could take a picture of the served food and send it off (along with a description) for processing. After processing, the consumer would receive the calories and ingredients associated with a serving of spaghetti and meatballs, which could then be downloaded into a food log. A dietitian could then analyze the food log in order to make diet recommendations. This article contains references that could be used by food scientists, dietitians, nutritionists, agricultural scientists, and instructors associated with family economics. Within computer science, this article touches on image databases, data mining, textual databases, image digitization, image capture, and computer vision associated with pattern recognition.
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