Abstract
The digital documentation of cultural heritage (CH) often requires interpretation and classification of a huge amount of images. The INCEPTION European project focuses on the development of tools and methodologies for obtaining 3D models of cultural heritage assets, enriched by semantic information and integration of both parts on a new H-BIM (Heritage - Building Information Modeling) platform. In this sense, the availability of automated techniques that allow the interpretation of photos and the search using semantic terms would greatly facilitate the work to develop the project. In this article the use of deep learning techniques, specifically the convolutional neural networks (CNNs) for analyzing images of cultural heritage is assessed. It is considered that the application of these techniques can make a significant contribution to the objectives sought in the INCEPTION project and, more generally, the digital documentation of cultural heritage.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sebastian, R., Bonsma, P., Bonsma, I., Ziri, A.E., Parenti, S., Lerones, P.M., Llamas, J., Maietti, F., Turillazzi, B., Iadanz, E.: Roadmap for IT research on a heritage-BIM interoperable platform within INCEPTION. In: SBE 16 MALTA, Europe and the Mediterranean Towards a Sustainable Built Environment (2016)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Shankar, S., Robertson, D., Ioannou, Y., Criminisi, A., Cipolla, R.: Refining architectures of deep convolutional neural networks. In: Conference on Computer Vision and Pattern Recognition (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision (2015). arXiv preprint arXiv:1512.00567
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jozefowicz, R., Jia, Y., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Schuster, M., Monga, R., Moore, S., Murray, D., Olah, C., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software tensorflow.org
Sharma, P., Schoemaker, M., Pan, D.: Automated Image Timestamp Inference Using Convolutional Neural Networks. Stanford University Report (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv technical report (2014)
Makantasis, K., Doulamis, A.D., Doulamis, N.D., Ioannides, M.: In the wild image retrieval and clustering for 3D cultural heritage landmarks reconstruction. Multimed. Tools Appl. 75(7), 3593–3629 (2016)
Acknowledgements
This research project has received funding from the EU’s H2020 Reflective framework programme for research and innovation under grant agreement no. 665220. This work was also supported by the Ministry of Science and Innovation, fundamental research project ref. DPI2014-56500 and Junta de Castilla y León ref. VA036U14.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Llamas, J., Lerones, P.M., Zalama, E., Gómez-García-Bermejo, J. (2016). Applying Deep Learning Techniques to Cultural Heritage Images Within the INCEPTION Project. In: Ioannides, M., et al. Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. EuroMed 2016. Lecture Notes in Computer Science(), vol 10059. Springer, Cham. https://doi.org/10.1007/978-3-319-48974-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-48974-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48973-5
Online ISBN: 978-3-319-48974-2
eBook Packages: Computer ScienceComputer Science (R0)