Publication: An ontology based semantic representation for Turkish Cuisine, Türk mutfaǧi için ontoloji tabanli semantik gösterim
| dc.contributor.author | Ergün, Övgü Öztürk | |
| dc.contributor.author | Öztürk, Bengü | |
| dc.contributor.institution | Ergün, Övgü Öztürk, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.contributor.institution | Öztürk, Bengü, İnönü Mah, Yeditepe University, Istanbul, Turkey | |
| dc.date.accessioned | 2025-10-05T16:08:04Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Following recent advances in digital technologies, many data in various domains have been transformed into digital world and shared with millions of users via social media and web technologies. As a result, big amount of data has presented many challenging problems in different fields, e.g internet of things, artificial intelligence. One of application areas is in food domain. Recognition of food category from images, automatic recipe retrieval from internet and analysis and matching of food images with recipes, ingredients, nutrition values bring cooperation of multi disciplines and technologies. In this work, for the first time, semantical analysis of Turkish Cuisine is held and various information related to food in Turkish Cuisine is structured in a hierarchical ontology model. A new database containing 50 different food categories and related images is constructed and linked with data such as food properties, recipes, etc. As a result, multimodal information retrieval can be achieved faster in a more semantic way. At the same time, food image classification with deep learning methods is performed and faster connection of recognized food category to related semantic data is provided. © 2018 Elsevier B.V., All rights reserved. | |
| dc.description.sponsorship | Aselsan | |
| dc.description.sponsorship | et al. | |
| dc.description.sponsorship | Huawei | |
| dc.description.sponsorship | IEEE Signal Processing Society | |
| dc.description.sponsorship | IEEE Turkey Section | |
| dc.description.sponsorship | Netas | |
| dc.identifier.conferenceName | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 | |
| dc.identifier.conferencePlace | Izmir, Altin Yunus Resort ve Thermal Hotel | |
| dc.identifier.doi | 10.1109/SIU.2018.8404617 | |
| dc.identifier.endpage | 4 | |
| dc.identifier.isbn | 9781538615010 | |
| dc.identifier.scopus | 2-s2.0-85050817131 | |
| dc.identifier.startpage | 1 | |
| dc.identifier.uri | https://doi.org/10.1109/SIU.2018.8404617 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14719/11607 | |
| dc.language.iso | tr | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject.authorkeywords | Deep Learning | |
| dc.subject.authorkeywords | Food Classificiation | |
| dc.subject.authorkeywords | Ontology | |
| dc.subject.authorkeywords | Pattern Recognition | |
| dc.subject.authorkeywords | Semantic Data | |
| dc.subject.authorkeywords | Turkish Cuisine | |
| dc.subject.authorkeywords | Deep Learning | |
| dc.subject.authorkeywords | Ontology | |
| dc.subject.authorkeywords | Pattern Recognition | |
| dc.subject.authorkeywords | Semantics | |
| dc.subject.authorkeywords | Application Area | |
| dc.subject.authorkeywords | Digital Technologies | |
| dc.subject.authorkeywords | Learning Methods | |
| dc.subject.authorkeywords | Multi Disciplines | |
| dc.subject.authorkeywords | Multimodal Information Retrieval | |
| dc.subject.authorkeywords | Semantic Data | |
| dc.subject.authorkeywords | Semantic Representation | |
| dc.subject.authorkeywords | Turkishs | |
| dc.subject.authorkeywords | Signal Processing | |
| dc.subject.indexkeywords | Deep learning | |
| dc.subject.indexkeywords | Ontology | |
| dc.subject.indexkeywords | Pattern recognition | |
| dc.subject.indexkeywords | Semantics | |
| dc.subject.indexkeywords | Application area | |
| dc.subject.indexkeywords | Digital technologies | |
| dc.subject.indexkeywords | Learning methods | |
| dc.subject.indexkeywords | Multi disciplines | |
| dc.subject.indexkeywords | Multimodal information retrieval | |
| dc.subject.indexkeywords | Semantic data | |
| dc.subject.indexkeywords | Semantic representation | |
| dc.subject.indexkeywords | Turkishs | |
| dc.subject.indexkeywords | Signal processing | |
| dc.title | An ontology based semantic representation for Turkish Cuisine, Türk mutfaǧi için ontoloji tabanli semantik gösterim | |
| dc.type | Conference Paper | |
| dcterms.references | Chen, Jingjing, Deep-based ingredient recognition for cooking recipe retrieval, pp. 32-41, (2016), Salvador, Amaia, Learning cross-modal embeddings for cooking recipes and food images, 2017-January, pp. 3068-3076, (2017), IEEE Transactions on Cybernetics, (2017), Çelik Ertuğrul, Duygu, FoodWiki: a Mobile App Examines Side Effects of Food Additives Via Semantic Web, Journal of Medical Systems, 40, 2, pp. 1-15, (2016), Gungor, Cem, Turkish cuisine: A benchmark dataset with Turkish meals for food recognition, (2017), Chu, Wei Ta, Food image description based on deep-based joint food category, ingredient, and cooking method recognition, pp. 109-114, (2017), Ciocca, Gianluigi, Food Recognition: A New Dataset, Experiments, and Results, IEEE Journal of Biomedical and Health Informatics, 21, 3, pp. 588-598, (2017), Mezgec, Simon, Nutrinet: A deep learning food and drink image recognition system for dietary assessment, Nutrients, 9, 7, (2017), Hassannejad, Hamid, Food image recognition using very deep convolutional networks, pp. 41-49, (2016), Myers, Austin, Im2Calories: Towards an automated mobile vision food diary, Proceedings of the IEEE International Conference on Computer Vision, 2015 International Conference on Computer Vision, ICCV 2015, pp. 1233-1241, (2015) | |
| dspace.entity.type | Publication | |
| local.indexed.at | Scopus | |
| person.identifier.scopus-author-id | 55292851100 | |
| person.identifier.scopus-author-id | 26433711800 |
