{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T06:04:53Z","timestamp":1762063493207,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T00:00:00Z","timestamp":1661558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","award":["739578"],"award-info":[{"award-number":["739578"]}]},{"name":"Government of the Republic of Cyprus","award":["739578"],"award-info":[{"award-number":["739578"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors.<\/jats:p>","DOI":"10.3390\/a15090305","type":"journal-article","created":{"date-parts":[[2022,8,28]],"date-time":"2022-08-28T21:22:56Z","timestamp":1661721776000},"page":"305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Systematic Approach for Developing a Robust Artwork Recognition Framework Using Smartphone Cameras"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3168-2350","authenticated-orcid":false,"given":"Zenonas","family":"Theodosiou","sequence":"first","affiliation":[{"name":"CYENS Center of Excellence, Nicosia 1016, Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7364-5799","authenticated-orcid":false,"given":"Marios","family":"Thoma","sequence":"additional","affiliation":[{"name":"CYENS Center of Excellence, Nicosia 1016, Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8555-260X","authenticated-orcid":false,"given":"Harris","family":"Partaourides","sequence":"additional","affiliation":[{"name":"CYENS Center of Excellence, Nicosia 1016, Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6841-8065","authenticated-orcid":false,"given":"Andreas","family":"Lanitis","sequence":"additional","affiliation":[{"name":"CYENS Center of Excellence, Nicosia 1016, Cyprus"},{"name":"Visual Media Computing Lab, Department of Multimedia and Graphic Arts, Cyprus University of Technology, Limassol 3036, Cyprus"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s00779-016-0994-9","article-title":"Visualizing Museum Visitors\u2019 Behavior: Where Do They Go and What Do They Do There?","volume":"21","author":"Lanir","year":"2017","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_2","first-page":"33","article-title":"Navigating Culture. Enhancing Visitor Museum Experience through Mobile Technologies. From Smartphone to Google Glass","volume":"7","author":"Tomiuc","year":"2014","journal-title":"J. Media Res."},{"key":"ref_3","first-page":"764","article-title":"Museum Apps Investigated: Availability, Content and Popularity","volume":"17","author":"Miluniec","year":"2020","journal-title":"e-Rev. Tour. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Theodosiou, Z., and Lanitis, A. (2019, January 9\u201310). Visual Lifelogs Retrieval: State of the Art and Future Challenges. Proceedings of the 2019 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), Larnaca, Cyprus.","DOI":"10.1109\/SMAP.2019.8864803"},{"key":"ref_5","unstructured":"Loizides, F., El Kater, A., Terlikas, C., Lanitis, A., and Michael, D. (2022, January 1\u20134). Presenting Cypriot Cultural Heritage in Virtual Reality: A User Evaluation. Proceedings of the Euro-Mediterranean Conference, Sousse, Tunisia."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Macdonald, S. (2006). Studying Visitors. A Companion to Museum Studies, Blackwell Publishing Ltd.. Chapter 22.","DOI":"10.1002\/9780470996836"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10796-012-9345-1","article-title":"Enhancing visitors\u2019 experience in art museums using mobile technologies","volume":"16","author":"Tesoriero","year":"2014","journal-title":"Inf. Syst. Front."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rashed, M.G., Suzuki, R., Yonezawa, T., Lam, A., Kobayashi, Y., and Kuno, Y. (2016, January 25\u201328). Tracking Visitors in a Real Museum for Behavioral Analysis. Proceedings of the 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), Sapporo, Japan.","DOI":"10.1109\/SCIS-ISIS.2016.0030"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mezzini, M., Limongelli, C., Sansonetti, G., and De Medio, C. (2020, January 14\u201317). Tracking Museum Visitors through Convolutional Object Detectors. Proceedings of the Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, Genoa, Italy.","DOI":"10.1145\/3386392.3399282"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ferrato, A., Limongelli, C., Mezzini, M., and Sansonetti, G. (2022). Using Deep Learning for Collecting Data about Museum Visitor Behavior. Appl. Sci., 12.","DOI":"10.3390\/app12020533"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s11412-017-9264-8","article-title":"Developing & Using Interaction Geography in a Museum","volume":"12","author":"Shapiro","year":"2017","journal-title":"Int. J. Comput. Support. Collab. Learn."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2872278","article-title":"The MIT Museum Glassware Prototype: Visitor Experience Exploration for Designing Smart Glasses","volume":"9","author":"Mason","year":"2016","journal-title":"J. Comput. Cult. Herit."},{"key":"ref_13","unstructured":"Zhang, R., Tas, Y., and Koniusz, P. (2017, January 19\u201322). Artwork Identification from Wearable Camera Images for Enhancing Experience of Museum Audiences. Proceedings of the MW17: Museums and the Web, Cleveland, OH, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3276772","article-title":"Egocentric Visitors Localization in Cultural Sites","volume":"12","author":"Ragusa","year":"2019","journal-title":"J. Comput. Cult. Herit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.culher.2019.07.019","article-title":"Exploiting artificial intelligence for digitally enriched museum visits","volume":"42","author":"Ioannakis","year":"2020","journal-title":"J. Cult. Herit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.culher.2017.11.008","article-title":"Predicting and grouping digitized paintings by style using unsupervised feature learning","volume":"31","author":"Gultepe","year":"2018","journal-title":"J. Cult. Herit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6513","DOI":"10.1007\/s11042-018-6387-5","article-title":"Art Painting Detection and Identification Based on Deep Learning and Image Local Features","volume":"78","author":"Hong","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nakahara, H., Yonekawa, H., Fujii, T., and Sato, S. (2018, January 25\u201327). A Lightweight YOLOv2: A Binarized CNN with A Parallel Support Vector Regression for an FPGA. Proceedings of the 2018 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA \u201918, Monterey, CA, USA.","DOI":"10.1145\/3174243.3174266"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Agapito, L., Bronstein, M.M., and Rother, C. (2014, January 6\u201312). In Search of Art. Proceedings of the Computer Vision\u2014ECCV 2014 Workshops, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-16181-5"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"123438","DOI":"10.1109\/ACCESS.2021.3110082","article-title":"A Deep Learning Approach to Ancient Egyptian Hieroglyphs Classification","volume":"9","author":"Barucci","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","unstructured":"Dalens, T., Sivic, J., Laptev, I., and Campedel, M. (2022, July 11). Painting Recognition from Wearable Cameras. Technical Report hal-01062126, INRIA. Available online: https:\/\/www.di.ens.fr\/willow\/research\/glasspainting\/."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Portaz, M., Kohl, M., Qu\u00e9not, G., and Chevallet, J. (2017, January 22\u201329). Fully Convolutional Network and Region Proposal for Instance Identification with Egocentric Vision. Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy.","DOI":"10.1109\/ICCVW.2017.281"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3092832","article-title":"Deep Artwork Detection and Retrieval for Automatic Context-Aware Audio Guides","volume":"13","author":"Seidenari","year":"2017","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_24","first-page":"588","article-title":"Artwork Identification in a Museum Environment: A Quantitative Evaluation of Factors Affecting Identification Accuracy","volume":"Volume 12642","author":"Ioannides","year":"2021","journal-title":"Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection"},{"key":"ref_25","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and <0.5 MB Model Size. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F.F. (2009, January 20\u201325). ImageNet: A Large-Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_27","unstructured":"(2021, March 17). ImageNet. Online. Available online: http:\/\/image-net.org."},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_29","unstructured":"Clark, A. (2022, July 11). Pillow (PIL Fork) Documentation, 2020. Version 8.0.0. Available online: https:\/\/pillow.readthedocs.io\/en\/stable\/releasenotes\/8.0.0.html."},{"key":"ref_30","unstructured":"(2022, July 11). XOIO-AIR. Cutout People\u2014Greenscreen Volume 1, Available online: https:\/\/xoio-air.de\/2012\/greenscreen_people_01\/."},{"key":"ref_31","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_34","unstructured":"Marcus, A. (2014). Applying the User Experience Questionnaire (UEQ) in Different Evaluation Scenarios. Design, User Experience, and Usability, Theories, Methods, and Tools for Designing the User Experience, Proceedings of the Third International Conference, Herakleion, Greece, 22\u201327 June 2014, Springer International Publishing."},{"key":"ref_35","first-page":"13","article-title":"Extending deep learning to new classes without retraining","volume":"Volume 11418","author":"Bishop","year":"2020","journal-title":"Proceedings of the Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Draelos, T.J., Miner, N.E., Lamb, C.C., Cox, J.A., Vineyard, C.M., Carlson, K.D., Severa, W.M., James, C.D., and Aimone, J.B. (2017, January 14\u201319). Neurogenesis deep learning: Extending deep networks to accommodate new classes. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7965898"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual lifelong learning with neural networks: A review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/9\/305\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:16:31Z","timestamp":1760141791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/9\/305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,27]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["a15090305"],"URL":"https:\/\/doi.org\/10.3390\/a15090305","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2022,8,27]]}}}