Abstract
The ability to identify the artworks that a museum visitor is looking at, using first-person images seamlessly captured by wearable cameras can be used as a means for invoking applications that provide information about the exhibits, and provide information about visitors’ activities. As part of our efforts to optimize the artwork recognition accuracy of an artwork identification system under development, an investigation aiming to determine the effect of different conditions on the artwork recognition accuracy in a gallery/exhibition environment is presented. Through the controlled introduction of different distractors in a virtual museum environment, it is feasible to assess the effect on the recognition performance of different conditions. The results of the experiment are important for improving the robustness of artwork recognition systems, and at the same time the conclusions of this work can provide specific guidelines to curators, museum professionals and visitors, that will enable the efficient identification of artworks, using images captured with wearable cameras in a museum environment.
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References
Banerji, S., Sinha, A.: Painting classification using a pre-trained convolutional neural network. In: Mukherjee, S. (ed.) ICVGIP 2016. LNCS, vol. 10481, pp. 168–179. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68124-5_15
Bolanos, M., Dimiccoli, M., Radeva, P.: Toward storytelling from visual lifelogging: an overview. IEEE Trans. Hum.-Mach. Syst. 47(1), 77–90 (2016). https://doi.org/10.1109/THMS.2016.2616296
Brancati, N., Caggianese, G., Frucci, M., Gallo, L., Neroni, P.: Experiencing touchless interaction with augmented content on wearable head-mounted displays in cultural heritage applications. Pers. Ubiquit. Comput. 21(2), 203–217 (2017). https://doi.org/10.1007/s00779-016-0987-8
Furnari, A., Battiato, S., Farinella, G.M.: Personal-location-based temporal segmentation of egocentric videos for lifelogging applications. J. Vis. Commun. Image Represent. 52, 1–12 (2018). https://doi.org/10.1016/j.jvcir.2018.01.019
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: alexnet-level accuracy with 50x fewer parameters and \(<\!0.5\) mb model size. arXiv preprint arXiv:1602.07360 (2016)
Loizides, F., El Kater, A., Terlikas, C., Lanitis, A., Michael, D.: Presenting Cypriot cultural heritage in virtual reality: a user evaluation. In: Ioannides, M., Magnenat-Thalmann, N., Fink, E., Žarnić, R., Yen, A.-Y., Quak, E. (eds.) EuroMed 2014. LNCS, vol. 8740, pp. 572–579. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13695-0_57
Panayiotou, S., Lanitis, A.: Paintings alive: a virtual reality-based approach for enhancing the user experience of art gallery visitors. In: Ioannides, M. (ed.) EuroMed 2016. LNCS, vol. 10059, pp. 240–247. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48974-2_27
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)
Ragusa, F., Furnari, A., Battiato, S., Signorello, G., Farinella, G.M.: Egocentric visitors localization in cultural sites. J. Comput. Cult. Heritage (JOCCH) 12(2), 1–19 (2019). https://doi.org/10.1145/3276772
Skoryukina, N.S., Nikolaev, D.P., Arlazarov, V.V.: 2d art recognition in uncontrolled conditions using one-shot learning. In: Eleventh International Conference on Machine Vision (ICMV 2018), vol. 11041, p. 110412E. International Society for Optics and Photonics (2019). https://doi.org/10.1117/12.2523017
Taverriti, G., Lombini, S., Seidenari, L., Bertini, M., Del Bimbo, A.: Real-time wearable computer vision system for improved museum experience. In: Proceedings of the 24th ACM international conference on Multimedia, pp. 703–704 (2016). https://doi.org/10.1145/2964284.2973813
Zhao, Z.Q., Zheng, P., Xu, S.t., Wu, X.: Object detection with deep learning: A review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019). https://doi.org/10.1109/TNNLS.2018.2876865
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 739578 complemented by the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development. We also like to thank the personnel of the State Gallery of Contemporary Cypriot Art for their support.
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Lanitis, A., Theodosiou, Z., Partaourides, H. (2021). Artwork Identification in a Museum Environment: A Quantitative Evaluation of Factors Affecting Identification Accuracy. In: Ioannides, M., Fink, E., Cantoni, L., Champion, E. (eds) Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. EuroMed 2020. Lecture Notes in Computer Science(), vol 12642. Springer, Cham. https://doi.org/10.1007/978-3-030-73043-7_50
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