19.7.3.7.3 Food Descriptions, Dishes, Recipe Generation

Chapter Contents (Back)
Food. Diet, recipies, calorise, etc.

Oliveira, L.[Luciano], Costa, V.[Victor], Neves, G.[Gustavo], Oliveira, T.[Talmai], Jorge, E.[Eduardo], Lizarraga, M.[Miguel],
A mobile, lightweight, poll-based food identification system,
PR(47), No. 5, 2014, pp. 1941-1952.
Elsevier DOI 1412
Food identification BibRef

Xu, R., Herranz, L., Jiang, S., Wang, S., Song, X., Jain, R.,
Geolocalized Modeling for Dish Recognition,
MultMed(17), No. 8, August 2015, pp. 1187-1199.
IEEE DOI 1506
Accuracy. Food dishes. Context. BibRef

Martinel, N.[Niki], Piciarelli, C.[Claudio], Micheloni, C.[Christian],
A supervised extreme learning committee for food recognition,
CVIU(148), No. 1, 2016, pp. 67-86.
Elsevier DOI 1606
Food recognition BibRef

Tatsuma, A.[Atsushi], Aono, M.[Masaki],
Food Image Recognition Using Covariance of Convolutional Layer Feature Maps,
IEICE(E99-D), No. 6, June 2016, pp. 1711-1715.
WWW Link. 1606
BibRef

Herranz, L.[Luis], Jiang, S.Q.[Shu-Qiang], Xu, R.H.[Rui-Han],
Modeling Restaurant Context for Food Recognition,
MultMed(19), No. 2, February 2017, pp. 430-440.
IEEE DOI 1702
Which restaurant helps reduce the possible foods. BibRef

Dehais, J., Anthimopoulos, M., Shevchik, S., Mougiakakou, S.,
Two-View 3D Reconstruction for Food Volume Estimation,
MultMed(19), No. 5, May 2017, pp. 1090-1099.
IEEE DOI 1704
Calibration BibRef

Pandey, P., Deepthi, A., Mandal, B., Puhan, N.B.,
FoodNet: Recognizing Foods Using Ensemble of Deep Networks,
SPLetters(24), No. 12, December 2017, pp. 1758-1762.
IEEE DOI 1712
convolution, food products, image recognition, neural nets, FoodNet, Indian food image database, automatic food recognition system, food recognition BibRef

Min, W., Jiang, S., Sang, J., Wang, H., Liu, X., Herranz, L.,
Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration,
MultMed(19), No. 5, May 2017, pp. 1100-1113.
IEEE DOI 1704
Correlation BibRef

Min, W., Bao, B.K., Mei, S., Zhu, Y., Rui, Y., Jiang, S.,
You Are What You Eat: Exploring Rich Recipe Information for Cross-Region Food Analysis,
MultMed(20), No. 4, April 2018, pp. 950-964.
IEEE DOI 1804
Analytical models, Computers, Cultural differences, Metadata, Pattern analysis, Probabilistic logic, Visualization, topic model BibRef

Ege, T.[Takumi], Yanai , K.[Keiji],
Image-Based Food Calorie Estimation Using Recipe Information,
IEICE(E101-D), No. 5, May 2018, pp. 1333-1341.
WWW Link. 1805
BibRef

Zheng, J.N.[Jian-Nan], Zou, L.[Liang], Wang, Z.J.[Z. Jane],
Mid-level deep Food Part mining for food image recognition,
IET-CV(12), No. 3, April 2018, pp. 298-304.
DOI Link 1804
BibRef

Heravi, E.J.[Elnaz Jahani], Aghdam, H.H.[Hamed Habibi], Puig, D.[Domenec],
An optimized convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods,
PRL(105), 2018, pp. 50-58.
Elsevier DOI 1804
Food classification, Convolutional neural networks, Neural network visualization, Deep learning, Spatial pyramid pooling BibRef

Horiguchi, S., Amano, S., Ogawa, M., Aizawa, K.,
Personalized Classifier for Food Image Recognition,
MultMed(20), No. 10, October 2018, pp. 2836-2848.
IEEE DOI 1810
feature extraction, food technology, image classification, image recognition, class mean classifier, deep feature BibRef

Yu, Q., Anzawa, M., Amano, S., Ogawa, M., Aizawa, K.,
Food Image Recognition by Personalized Classifier,
ICIP18(171-175)
IEEE DOI 1809
Feature extraction, Image recognition, Optimization, Databases, Artificial neural networks, Training, Adaptation models, classifier adaptation BibRef

Ciocca, G.[Gianluigi], Napoletano, P.[Paolo], Schettini, R.[Raimondo],
CNN-based features for retrieval and classification of food images,
CVIU(176-177), 2018, pp. 70-77.
Elsevier DOI 1812
Food retrieval, Food dataset, Food recognition, CNN-based features BibRef

Aguilar, E., Remeseiro, B., Bolaños, M., Radeva, P.,
Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants,
MultMed(20), No. 12, December 2018, pp. 3266-3275.
IEEE DOI 1812
catering industry, convolution, feedforward neural nets, food products, image segmentation, object detection, convolutional neural networks BibRef

Anzawa, M.[Masashi], Amano, S.[Sosuke], Yamakata, Y.[Yoko], Motonaga, K.[Keiko], Kamei, A.[Akiko], Aizawa, K.[Kiyoharu],
Recognition of Multiple Food Items in A Single Photo for Use in A Buffet-Style Restaurant,
IEICE(E102-D), No. 2, February 2019, pp. 410-414.
WWW Link. 1902
BibRef

Aguilar, E.[Eduardo], Bolaños, M.[Marc], Radeva, P.[Petia],
Regularized uncertainty-based multi-task learning model for food analysis,
JVCIR(60), 2019, pp. 360-370.
Elsevier DOI 1903
Multi-task models, Uncertainty modeling, Convolutional neural networks, Food image analysis, Cuisine recognition BibRef

Ege, T.[Takumi], Yanai, K.[Keiji],
Simultaneous Estimation of Dish Locations and Calories with Multi-Task Learning,
IEICE(E102-D), No. 7, July 2019, pp. 1240-1246.
WWW Link. 1907
BibRef
Earlier:
Simultaneous estimation of food categories and calories with multi-task CNN,
MVA17(198-201)
DOI Link 1708
Correlation, Estimation, Image recognition, MISO, Organizations, Standards, Training BibRef

Shimoda, W.[Wataru], Yanai, K.[Keiji],
Webly-Supervised Food Detection with Foodness Proposal,
IEICE(E102-D), No. 7, July 2019, pp. 1230-1239.
WWW Link. 1907
BibRef

Takahashi, K.[Kazuma], Hattori, T.[Tatsumi], Doman, K.[Keisuke], Kawanishi, Y.[Yasutomo], Hirayama, T.[Takatsugu], Ide, I.[Ichiro], Deguchi, D.[Daisuke], Murase, H.[Hiroshi],
Estimation of the Attractiveness of Food Photography Based on Image Features,
IEICE(E102-D), No. 8, August 2019, pp. 1590-1593.
WWW Link. 1908
BibRef

Zoran, A.,
Cooking With Computers: The Vision of Digital Gastronomy,
PIEEE(107), No. 8, August 2019, pp. 1467-1473.
IEEE DOI 1908
BibRef

Jiang, S., Min, W., Liu, L., Luo, Z.,
Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition,
IP(29), No. 1, 2020, pp. 265-276.
IEEE DOI 1910
computer vision, convolutional neural nets, feature extraction, image classification, image fusion, image representation, convolutional neural networks BibRef

Yu, Q.[Qing], Anzawa, M.[Masashi], Amano, S.[Sosuke], Aizawa, K.[Kiyoharu],
Personalized Food Image Classifier Considering Time-Dependent and Item-Dependent Food Distribution,
IEICE(E102-D), No. 11, November 2019, pp. 2120-2126.
WWW Link. 1912
BibRef

Furtado, P.[Pedro], Caldeira, M.[Manuel], Martins, P.[Pedro],
Human Visual System vs Convolution Neural Networks in food recognition task: An empirical comparison,
CVIU(191), 2020, pp. 102878.
Elsevier DOI 2002
Deep learning, Machine learning, Food recognition BibRef

Gao, X., Feng, F., He, X., Huang, H., Guan, X., Feng, C., Ming, Z., Chua, T.,
Hierarchical Attention Network for Visually-Aware Food Recommendation,
MultMed(22), No. 6, June 2020, pp. 1647-1659.
IEEE DOI 2005
Visualization, Recommender systems, Collaboration BibRef

Miasnikov, E.[Evgeniy], Savchenko, A.[Andrey],
Detection and Recognition of Food in Photo Galleries for Analysis of User Preferences,
ICIAR20(I:83-94).
Springer DOI 2007
BibRef

Aguilar, E.[Eduardo], Radeva, P.[Petia],
Uncertainty-aware integration of local and flat classifiers for food recognition,
PRL(136), 2020, pp. 237-243.
Elsevier DOI 2008
BibRef
Earlier:
Food Recognition by Integrating Local and Flat Classifiers,
IbPRIA19(I:65-74).
Springer DOI 1910
CNNs, Deep learning, Epistemic uncertainty, Image classification, Food recognition BibRef

Qaraqe, M.[Marwa], Usman, M.[Muhammad], Ahmad, K.[Kashif], Sohail, A.[Amir], Boyaci, A.[Ali],
Automatic food recognition system for middle-eastern cuisines,
IET-IPR(14), No. 11, September 2020, pp. 2469-2479.
DOI Link 2009
BibRef

Min, W., Jiang, S., Jain, R.,
Food Recommendation: Framework, Existing Solutions, and Challenges,
MultMed(22), No. 10, October 2020, pp. 2659-2671.
IEEE DOI 2009
Biomedical monitoring, Diabetes, Heart rate, Temperature sensors, Analytical models, Blood pressure, Artificial intelligence, health information management BibRef

Jiji, G.W.[G. Wiselin], Rajesh, A.,
Food Sustenance Estimation Using Food Image,
IJIG(20), No. 4, October 2020, pp. 2050034.
DOI Link 2011
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Marín, J.[Javier], Biswas, A.[Aritro], Ofli, F.[Ferda], Hynes, N.[Nicholas], Salvador, A.[Amaia], Aytar, Y.[Yusuf], Weber, I.[Ingmar], Torralba, A.B.[Antonio B.],
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images,
PAMI(43), No. 1, January 2021, pp. 187-203.
IEEE DOI 2012
BibRef
Earlier: A5, A4, A6, A1, A3, A7, A8, Only:
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images,
CVPR17(3068-3076)
IEEE DOI 1711
Task analysis, Semantics, Data models, Search engines, Neural networks, Deep learning, Computer vision, Cross-modal, food images. Data models, Image representation, Semantics, Tools, Training BibRef

Chen, J., Zhu, B., Ngo, C.W., Chua, T.S., Jiang, Y.G.,
A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition,
IP(30), 2021, pp. 1514-1526.
IEEE DOI 2101
Image recognition, Visualization, Phase frequency detectors, Image segmentation, Fish, Deep learning, Shape, Food images, deep learning BibRef

Liu, C.X.[Cheng-Xu], Liang, Y.Z.[Yuan-Zhi], Xue, Y.[Yao], Qian, X.M.[Xue-Ming], Fu, J.[Jianlong],
Food and Ingredient Joint Learning for Fine-Grained Recognition,
CirSysVideo(31), No. 6, June 2021, pp. 2480-2493.
IEEE DOI 2106
Task analysis, Birds, Automobiles, Training, Image recognition, Dogs, Visualization, Fine-grained, food classification, joint learning BibRef


Zhao, H.[Heng], Yap, K.H.[Kim-Hui], Kot, A.C.[Alex Chichung],
Fusion Learning using Semantics and Graph Convolutional Network for Visual Food Recognition,
WACV21(1710-1719)
IEEE DOI 2106
Training, Knowledge engineering, Visualization, Image recognition, Social networking (online), Conferences BibRef

Ruede, R.[Robin], Heusser, V.[Verena], Frank, L.[Lukas], Roitberg, A.[Alina], Haurilet, M.[Monica], Stiefelhagen, R.[Rainer],
Multi-Task Learning for Calorie Prediction on a Novel Large-Scale Recipe Dataset Enriched with Nutritional Information,
ICPR21(4001-4008)
IEEE DOI 2105
Proteins, Annotations, Neural networks, Estimation, Manuals, Benchmark testing, calorie estimation, ingredients BibRef

Lu, Y.[Ya], Stathopoulou, T.[Thomai], Mougiakakou, S.[Stavroula],
Partially Supervised Multi-Task Network for Single-View Dietary Assessment,
ICPR21(8156-8163)
IEEE DOI 2105
Training, Structure from motion, Volume measurement, Semantics, Pipelines, Estimation BibRef

Li, J.[Jiatong], Han, F.[Fangda], Guerrero, R.[Ricardo], Pavlovic, V.[Vladimir],
Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images,
ICPR21(10343-10350)
IEEE DOI 2105
Deep learning, Measurement, Pattern recognition, Internet, Task analysis BibRef

Aguilar, E.[Eduardo], Nagarajan, B.[Bhalaji], Khantun, R.[Rupali], Bolaños, M.[Marc], Radeva, P.[Petia],
Uncertainty-Aware Data Augmentation for Food Recognition,
ICPR21(4017-4024)
IEEE DOI 2105
Training, Deep learning, Uncertainty, Image recognition, Computational modeling, Training data BibRef

Zhou, P.F.[Peng-Fei], Bai, C.[Cong], Ying, K.[Kaining], Xia, J.[Jie], Huang, L.X.[Li-Xin],
RWMF: A Real-World Multimodal Foodlog Database,
ICPR21(962-968)
IEEE DOI 2105
Databases, Biometrics (access control), Sociology, Pattern recognition, Glucose, Diabetes, Classification algorithms BibRef

Lei, J.[Jiabao], Qiu, J.N.[Jia-Ning], Lo, F.P.W.[Frank P.-W.], Lo, B.[Benny],
Assessing Individual Dietary Intake in Food Sharing Scenarios with Food and Human Pose Detection,
MADiMa20(549-557).
Springer DOI 2103
BibRef

Stanik III, P.[Paul], Morris, B.T.[Brendan Tran], Serafica, R.[Reimund], Webber, K.H.[Kelly Harmon],
Mysnapfoodlog: Culturally Sensitive Food Photo-logging App for Dietary Biculturalism Studies,
ISVC20(II:470-482).
Springer DOI 2103
BibRef

Okamoto, K.[Kaimu], Yanai, K.[Keiji],
Uec-foodpix Complete: A Large-scale Food Image Segmentation Dataset,
MADiMa20(647-659).
Springer DOI 2103
BibRef

Pandey, V.[Vaibhav], Rostami, A.[Ali], Nag, N.[Nitish], Jain, R.[Ramesh],
Event Mining Driven Context-aware Personal Food Preference Modelling,
MADiMa20(660-676).
Springer DOI 2103
BibRef

Nagarajan, B.[Bhalaji], Aguilar, E.[Eduardo], Radeva, P.[Petia],
S2ml-tl Framework for Multi-label Food Recognition,
MADiMa20(629-646).
Springer DOI 2103
BibRef

Mao, R.[Runyu], He, J.P.[Jiang-Peng], Shao, Z.[Zeman], Yarlagadda, S.K.[Sri Kalyan], Zhu, F.Q.[Feng-Qing],
Visual Aware Hierarchy Based Food Recognition,
MADiMa20(571-598).
Springer DOI 2103
BibRef

Selamat, N.A.[Nur Asmiza], Ali, S.H.M.[Sawal Hamid Md.],
Analysis of Chewing Signals Based on Chewing Detection Using Proximity Sensor for Diet Monitoring,
MADiMa20(599-616).
Springer DOI 2103
BibRef

Papathanail, I.[Ioannis], Lu, Y.[Ya], Ghosh, A.[Arindam], Mougiakakou, S.[Stavroula],
Food Recognition in the Presence of Label Noise,
MADiMa20(617-628).
Springer DOI 2103
BibRef

Artese, M.T.[Maria Teresa], Ciocca, G.[Gianluigi], Gagliardi, I.[Isabella],
Analysis of Traditional Italian Food Recipes: Experiments and Results,
MADiMa20(677-690).
Springer DOI 2103
BibRef

Shao, H., Mu, J., Tang, R., Chen, X., Liu, M.,
Research on Automatic Dish Recognition Algorithm Based on Deep Learning,
CVIDL20(566-570)
IEEE DOI 2102
catering industry, computer vision, convolutional neural nets, feature extraction, image classification, image representation, Dishes identification BibRef

Kasturi, S., Le Moan, S., Bailey, D., Smith, J.,
Heating Patterns Recognition in Industrial Microwave-Processed Foods,
IVCNZ20(1-5)
IEEE DOI 2012
Reflectivity, Electromagnetic heating, Microwave theory and techniques, kinetics study BibRef

Gallo, I.[Ignazio], Ria, G.[Gianmarco], Landro, N.[Nicola], La Grassa, R.[Riccardo],
Image and Text fusion for UPMC Food-101 using BERT and CNNs,
IVCNZ20(1-6)
IEEE DOI 2012
Adaptation models, Visualization, Stacking, Bit error rate, Data models, Proposals, Noise measurement BibRef

Theodoridis, T., Solachidis, V., Dimitropoulos, K., Daras, P.,
A Cross-Modal Variational Framework For Food Image Analysis,
ICIP20(3244-3248)
IEEE DOI 2011
Decoding, Task analysis, Training, Image recognition, Gaussian distribution, Network architecture, food analysis BibRef

Han, F., Guerrero, R., Pavlovic, V.,
CookGAN: Meal Image Synthesis from Ingredients,
WACV20(1439-1447)
IEEE DOI 2006
Feature extraction, Training, Neural networks, Generators, Image resolution, Computational modeling BibRef

Wang, H.[Hao], Lin, G.S.[Guo-Sheng], Hoi, S.C.H.[Steven C. H.], Miao, C.Y.[Chun-Yan],
Structure-aware Generation Network for Recipe Generation from Images,
ECCV20(XXVII:359-374).
Springer DOI 2011
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Fu, H., Wu, R., Liu, C., Sun, J.,
MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model,
CVPR20(14558-14568)
IEEE DOI 2008
Training, Task analysis, Visualization, Correlation, Computational modeling, Computer architecture, Computer vision BibRef

Sun, J., Radecka, K., Zilic, Z.,
Exploring Better Food Detection via Transfer Learning,
MVA19(1-6)
DOI Link 1911
computer vision, convolutional neural nets, image classification, learning (artificial intelligence), neural net architecture, Proposals BibRef

Jelodar, A.B.[Ahmad Babaeian], Sun, Y.[Yu],
Joint Object and State Recognition Using Language Knowledge,
ICIP19(3352-3356)
IEEE DOI 1910
Cooking related images. State Classification, Transfer Learning, joint object and state classification, Concept-Net BibRef

Salvador, A.[Amaia], Drozdzal, M.[Michal], Giro-i-Nieto, X.[Xavier], Romero, A.[Adriana],
Inverse Cooking: Recipe Generation From Food Images,
CVPR19(10445-10454).
IEEE DOI 2002
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Wang, H.[Hao], Sahoo, D.[Doyen], Liu, C.H.[Cheng-Hao], Lim, E.P.[Ee-Peng], Hoi, S.C.H.[Steven C. H.],
Learning Cross-Modal Embeddings With Adversarial Networks for Cooking Recipes and Food Images,
CVPR19(11564-11573).
IEEE DOI 2002
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Konstantinidis, D.[Dimitrios], Dimitropoulos, K.[Kosmas], Ioakimidis, I.[Ioannis], Langlet, B.[Billy], Daras, P.[Petros],
A Deep Network for Automatic Video-based Food Bite Detection,
CVS19(586-595).
Springer DOI 1912
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Donadello, I.[Ivan], Dragoni, M.[Mauro],
Ontology-Driven Food Category Classification in Images,
CIAP19(II:607-617).
Springer DOI 1909
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Allegra, D.[Dario], Erba, D.[Daniela], Farinella, G.M.[Giovanni Maria], Grazioso, G.[Giovanni], Maci, P.D.[Paolo Danilo], Stanco, F.[Filippo], Tomaselli, V.[Valeria],
Learning to Rank Food Images,
CIAP19(II:629-639).
Springer DOI 1909
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Bolaños, M.[Marc], Valdivia, M.[Marc], Radeva, P.[Petia],
Where and What Am I Eating? Image-Based Food Menu Recognition,
MultLearnApp18(VI:590-605).
Springer DOI 1905
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Ciocca, G.[Gianluigi], Mazzini, D.[Davide], Schettini, R.[Raimondo],
Evaluating CNN-Based Semantic Food Segmentation Across Illuminants,
CCIW19(247-259).
Springer DOI 1905
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Fang, S., Liu, C., Tahboub, K., Zhu, F., Delp, E.J., Boushey, C.J.,
cTADA: The Design of a Crowdsourcing Tool for Online Food Image Identification and Segmentation,
Southwest18(25-28)
IEEE DOI 1809
Image segmentation, Tools, Noise measurement, Crowdsourcing, Task analysis, Systematics, Training data, Dietary Assessment, Groundtruth Segmentation BibRef

Fang, S., Shao, Z., Mao, R., Fu, C., Delp, E.J., Zhu, F., Kerr, D.A., Boushey, C.J.,
Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks,
ICIP18(251-255)
IEEE DOI 1809
Estimation, Image segmentation, Generative adversarial networks, Task analysis, Image-to-Energy Mapping BibRef

Chen, H., Wang, J., Qi, Q., Li, Y., Sun, H.,
Bilinear CNN Models for Food Recognition,
DICTA17(1-6)
IEEE DOI 1804
computer vision, feature extraction, feedforward neural nets, image classification, learning (artificial intelligence), Image recognition BibRef

Chen, J.J.[Jing-Jing], Pang, L.[Lei], Ngo, C.W.[Chong-Wah],
Cross-Modal Recipe Retrieval: How to Cook this Dish?,
MMMod17(I: 588-600).
Springer DOI 1701
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Wang, Y., Zhu, F., Boushey, C.J., Delp, E.J.,
Weakly supervised food image segmentation using class activation maps,
ICIP17(1277-1281)
IEEE DOI 1803
Cancer, Image segmentation, Kernel, Semantics, Supervised learning, Task analysis, Training, dietary assessment, graph model, weakly supervised learning BibRef

Ming, Z.Y.[Zhao-Yan], Chen, J.J.[Jing-Jing], Cao, Y.[Yu], Forde, C.[Ciarán], Ngo, C.W.[Chong-Wah], Chua, T.S.[Tat Seng],
Food Photo Recognition for Dietary Tracking: System and Experiment,
MMMod18(II:129-141).
Springer DOI 1802
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Tanno, R.[Ryosuke], Ege, T.[Takumi], Yanai, K.[Keiji],
AR DeepCalorieCam: An iOS App for Food Calorie Estimation with Augmented Reality,
MMMod18(II:352-356).
Springer DOI 1802
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Christ, P.F., Schlecht, S., Ettlinger, F., Grün, F., Heinle, C., Tatavatry, S., Ahmadi, S.A., Diepold, K., Menze, B.H.,
Diabetes60: Inferring Bread Units From Food Images Using Fully Convolutional Neural Networks,
ACVR17(1526-1535)
IEEE DOI 1802
Cameras, Computer vision, Diabetes, BibRef

Aguilar, E.[Eduardo], Bolaños, M.[Marc], Radeva, P.[Petia],
Food Recognition Using Fusion of Classifiers Based on CNNs,
CIAP17(II:213-224).
Springer DOI 1711
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Razali, M.N.[Mohd Norhisham], Manshor, N.[Noridayu], Halin, A.A.[Alfian Abdul], Yaakob, R.[Razali], Mustapha, N.[Norwati],
Food Category Recognition Using SURF and MSER Local Feature Representation,
IVIC17(212-223).
Springer DOI 1711
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Einarsson, G.[Gudmundur], Jensen, J.N.[Janus N.], Paulsen, R.R.[Rasmus R.], Einarsdottir, H.[Hildur], Ersbøll, B.K.[Bjarne K.], Dahl, A.B.[Anders B.], Christensen, L.B.[Lars Bager],
Foreign Object Detection in Multispectral X-ray Images of Food Items Using Sparse Discriminant Analysis,
SCIA17(I: 350-361).
Springer DOI 1706
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Bolaños, M., Radeva, P.,
Simultaneous food localization and recognition,
ICPR16(3140-3145)
IEEE DOI 1705
Cameras, Computer vision, Image recognition, Kernel, Pattern recognition, Proposals, Training BibRef

Moulos, I.[Ioannis], Maramis, C.[Christos], Ioakimidis, I.[Ioannis], van den Boer, J.[Janet], Nolstam, J.[Jenny], Mars, M.[Monica], Bergh, C.[Cecilia], Maglaveras, N.[Nicos],
Objective and Subjective Meal Registration via a Smartphone Application,
MADiMa15(409-416).
Springer DOI 1511
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Caon, M.[Maurizio], Carrino, S.[Stefano], Prinelli, F.[Federica], Ciociola, V.[Valentina], Adorni, F.[Fulvio], Lafortuna, C.[Claudio], Tabozzi, S.[Sarah], Serrano, J.[José], Condon, L.[Laura], Khaled, O.A.[Omar Abou], Mugellini, E.[Elena],
Towards an Engaging Mobile Food Record for Teenagers,
MADiMa15(417-424).
Springer DOI 1511
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Waltner, G.[Georg], Schwarz, M.[Michael], Ladstätter, S.[Stefan], Weber, A.[Anna], Luley, P.[Patrick], Bischof, H.[Horst], Lindschinger, M.[Meinrad], Schmid, I.[Irene], Paletta, L.[Lucas],
MANGO: Mobile Augmented Reality with Functional Eating Guidance and Food Awareness,
MADiMa15(425-432).
Springer DOI 1511
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Yang, H.X.[Hai-Xiang], Zhang, D.[Dong], Lee, D.J.[Dah-Jye], Huang, M.J.[Min-Jie],
A Sparse Representation Based Classification Algorithm for Chinese Food Recognition,
ISVC16(II: 3-10).
Springer DOI 1701
BibRef

Myers, A., Johnston, N., Rathod, V., Korattikara, A., Gorban, A., Silberman, N., Guadarrama, S., Papandreou, G., Huang, J., Murphy, K.,
Im2Calories: Towards an Automated Mobile Vision Food Diary,
ICCV15(1233-1241)
IEEE DOI 1602
Cameras BibRef

Martinel, N., Foresti, G.L., Micheloni, C.,
Wide-Slice Residual Networks for Food Recognition,
WACV18(567-576)
IEEE DOI 1806
computer vision, feature extraction, food technology, image classification, image representation, Visualization BibRef

Martinel, N., Piciarelli, C., Micheloni, C., Foresti, G.L.,
A Structured Committee for Food Recognition,
ACVR15(484-492)
IEEE DOI 1602
Diseases BibRef

Li, Y.[Ying], Sheopuri, A.[Anshul],
Applying image analysis to assess food aesthetics and uniqueness,
ICIP15(311-314)
IEEE DOI 1512
Computational aesthetics BibRef

Wang, Y.[Yu], He, Y.[Ye], Zhu, F.Q.[Feng-Qing], Boushey, C.[Carol], Delp, E.J.[Edward J.],
The Use of Temporal Information in Food Image Analysis,
MADiMa15(317-325).
Springer DOI 1511
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Knez, S.[Simon], Šajn, L.[Luka],
Food Object Recognition Using a Mobile Device: State of the Art,
MADiMa15(366-374).
Springer DOI 1511
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Pouladzadeh, P.[Parisa], Yassine, A.[Abdulsalam], Shirmohammadi, S.[Shervin],
FooDD: Food Detection Dataset for Calorie Measurement Using Food Images,
MADiMa15(441-448).
Springer DOI 1511
BibRef

Ciocca, G.[Gianluigi], Napoletano, P.[Paolo], Schettini, R.[Raimondo],
Food Recognition and Leftover Estimation for Daily Diet Monitoring,
MADiMa15(334-341).
Springer DOI 1511
BibRef

Matsunaga, H.[Hiroki], Doman, K.[Keisuke], Hirayama, T.[Takatsugu], Ide, I.[Ichiro], Deguchi, D.[Daisuke], Murase, H.[Hiroshi],
Tastes and Textures Estimation of Foods Based on the Analysis of Its Ingredients List and Image,
MADiMa15(326-333).
Springer DOI 1511
BibRef

Mazzei, A.[Alessandro], Anselma, L.[Luca], de Michieli, F.[Franco], Bolioli, A.[Andrea], Casuu, M.[Matteo], Gerbrandy, J.[Jelle], Lunardi, I.[Ivan],
Mobile Computing and Artificial Intelligence for Diet Management,
MADiMa15(342-349).
Springer DOI 1511
BibRef

Kagaya, H.[Hokuto], Aizawa, K.[Kiyoharu],
Highly Accurate Food/Non-Food Image Classification Based on a Deep Convolutional Neural Network,
MADiMa15(350-357).
Springer DOI 1511
BibRef

Farinella, G.M.[Giovanni Maria], Moltisanti, M.[Marco], Battiato, S.[Sebastiano],
Food Recognition Using Consensus Vocabularies,
MADiMa15(384-392).
Springer DOI 1511
BibRef

Kawano, Y.[Yoshiyuki], Yanai, K.[Keiji],
Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation,
TASKCV14(3-17).
Springer DOI 1504
BibRef

Farinella, G.M.[Giovanni Maria], Allegra, D.[Dario], Stanco, F.[Filippo], Battiato, S.[Sebastiano],
On the Exploitation of One Class Classification to Distinguish Food Vs Non-Food Images,
MADiMa15(375-383).
Springer DOI 1511
BibRef
And: A1, A2, A3, Only:
A Benchmark Dataset to Study the Representation of Food Images,
ACVR14(584-599).
Springer DOI 1504
BibRef

Beijbom, O.[Oscar], Joshi, N.[Neel], Morris, D.[Dan], Saponas, S.[Scott], Khullar, S.[Siddharth],
Menu-Match: Restaurant-Specific Food Logging from Images,
WACV15(844-851)
IEEE DOI 1503
Computer vision BibRef

Bettadapura, V.[Vinay], Thomaz, E.[Edison], Parnami, A.[Aman], Abowd, G.D.[Gregory D.], Essa, I.[Irfan],
Leveraging Context to Support Automated Food Recognition in Restaurants,
WACV15(580-587)
IEEE DOI 1503
Cameras BibRef

He, Y.[Ye], Xu, C.[Chang], Khanna, N.[Nitin], Boushey, C.J.[Carol J.], Delp, E.J.[Edward J.],
Analysis of food images: Features and classification,
ICIP14(2744-2748)
IEEE DOI 1502
Accuracy BibRef

Farinella, G.M.[Giovanni Maria], Moltisanti, M.[Marco], Battiato, S.[Sebastiano],
Classifying food images represented as Bag of Textons,
ICIP14(5212-5216)
IEEE DOI 1502
Accuracy BibRef

Xu, C.[Chang], He, Y.[Ye], Khanna, N.[Nitin], Boushey, C.J.[Carol J.], Delp, E.J.[Edward J.],
Model-based food volume estimation using 3D pose,
ICIP13(2534-2538)
IEEE DOI 1412
BibRef
Earlier: A2, A1, A3, A4, A5:
Context based food image analysis,
ICIP13(2748-2752)
IEEE DOI 1412
3D model rendering. Contextual Information BibRef

Kawano, Y.[Yoshiyuki], Yanai, K.[Keiji],
Offline 1000-Class Classification on a Smartphone,
IWMV14(193-194)
IEEE DOI 1409
BibRef

Kawano, Y.[Yoshiyuki], Yanai, K.[Keiji],
FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher Vector,
MMMod14(II: 369-373).
Springer DOI 1405
BibRef
Earlier:
Rapid Mobile Object Recognition Using Fisher Vector,
ACPR13(476-480)
IEEE DOI 1408
BibRef
And:
Real-Time Mobile Food Recognition System,
IWMV13(1-7)
IEEE DOI 1309
Android application;food recognition;mobile image recognition image classification. BibRef

Matsuda, Y.[Yuji], Yanai, K.[Keiji],
Multiple-food recognition considering co-occurrence employing manifold ranking,
ICPR12(2017-2020).
WWW Link. 1302
BibRef

Joutou, T.[Taichi], Yanai, K.[Keiji],
A food image recognition system with Multiple Kernel Learning,
ICIP09(285-288).
IEEE DOI 0911
BibRef

Bosch, M.[Marc], Zhu, F.Q.[Feng-Qing], Khanna, N.[Nitin], Boushey, C.J.[Carol J.], Delp, E.J.[Edward J.],
Combining global and local features for food identification in dietary assessment,
ICIP11(1789-1792).
IEEE DOI 1201
BibRef
Earlier: A2, A1, A4, A5, Only:
An image analysis system for dietary assessment and evaluation,
ICIP10(1853-1856).
IEEE DOI 1009
Image based. BibRef

Yang, S.L.[Shulin Lynn], Chen, M.[Mei], Pomerleau, D.[Dean], Sukthankar, R.[Rahul],
Food recognition using statistics of pairwise local features,
CVPR10(2249-2256).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef

Chen, M.[Mei], Dhingra, K.[Kapil], Wu, W.[Wen], Yang, L.[Lei], Sukthankar, R.[Rahul], Yang, J.[Jie],
PFID: Pittsburgh fast-food image dataset,
ICIP09(289-292).
IEEE DOI 0911
BibRef

Puri, M.[Manika], Zhu, Z.W.[Zhi-Wei], Yu, Q.[Qian], Divakaran, A.[Ajay], Sawhney, H.[Harpreet],
Recognition and volume estimation of food intake using a mobile device,
WACV09(1-8).
IEEE DOI 0912
BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Weed Detection, Close Range .


Last update:Sep 19, 2021 at 21:11:01