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      Saving Cultural Heritage with Digital Make-Believe: Machine Learning and Digital Techniques to the Rescue

      Published
      proceedings-article
      1 , 2 , 1 , 1
      Electronic Visualisation and the Arts (EVA 2017) (EVA)
      Electronic Visualisation and the Arts
      11 – 13 July 2017
      Semantic Archive, Image Retrieval, Cultural Heritage, Data Mining, Deep Learning
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            Abstract

            The application of digital methods for content-based curation and dissemination of cultural heritage data offers unique advantages for physical sites at risk of damage. In areas affected by 2011 Arab spring, digital may be the only approach to create believable cultural experiences. We propose a framework incorporating computational methods such as: digital image processing, multi-lingual text analysis, and 3D modelling, to facilitate enhanced data archive, federated search, and analysis. Potential use cases include experiential search, damage assessment, virtual site reconstruction, and provision of augmented information for education and cultural preservation. This paper presents initial findings from an empirical evaluation of existing scene classification methods, applied to detection of cultural heritage sites in the Palmyra region. Results indicate that deep learning offers an appropriate solution to semantic annotation of publicly available cultural heritage image data.

            Content

            Author and article information

            Contributors
            Conference
            July 2017
            July 2017
            : 1-5
            Affiliations
            [ 1 ]Faculty of Computer Science, University of Sunderland, Sunderland, SR1 3SD, UK
            [ 2 ]Physics Department, Faculty of Science at Qena, South Valley University, 83523, Egypt
            Article
            10.14236/ewic/HCI2017.97
            ca02feae-6dfc-4b26-8f73-9f80b52f3169
            © Yasser et al. Published by BCS Learning and Development. Proceedings of British HCI 2017 – Digital Make-Believe, Sunderland, UK.

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Electronic Visualisation and the Arts (EVA 2017)
            EVA
            London, UK
            11 – 13 July 2017
            Electronic Workshops in Computing (eWiC)
            Electronic Visualisation and the Arts
            History
            Product

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2017.97
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            Semantic Archive,Data Mining,Image Retrieval,Deep Learning,Cultural Heritage

            reference

            1. [1] 2010 A definition of cultural heritage: From the tangible to the intangible Journal of Cultural Heritage 11 3 321 324

            2. [2] 2007 Cultural heritage and human rights Cultural heritage and human rights 3 29 Springer New York

            3. [3] 2016 Cultural heritage and the challenge of sustainability Routledge

            4. [4] 2016 Applying Deep Learning Techniques to Cultural Heritage Images Within the INCEPTION Project et al Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection EuroMed 2016 Lecture Notes in Computer Science 10059 Springer, Cham.

            5. [5] 2017 Integrated Data Capturing Requirements For 3d Semantic Modelling of Cultural Heritage: The Inception Protocol, Int Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3 251 257 doi:10.5194/isprs-archives-XLII-2-W3-251-2017.

            6. [6] 2016 Refining Architectures of Deep Convolutional Neural Networks Conference on Computer Vision and Pattern Recognition.

            7. [7] SCA http://www.sca-egypt.org/eng/main.htm 12/5/2017

            8. [8] Visual Categorization with Bags of Keypoints Workshop on Statistical Learning in Computer Vision. ECCV 1 1-22 12

            9. [9] et al "Imagenet: A large-scale hierarchical image database." Computer Vision and Pattern Recognition, 2009 CVPR 2009 IEEE Conference on. IEEE 2009

            10. [10] "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 2012

            11. [11] Cloud Vision API https://cloud.google.com/vision/

            12. [12] others 2015 Keras https://github.com/fchollet/keras GitHub

            13. [13] 2016 Model-Free Episodic Control

            14. [14] 2017 Mode Free Episodic Control

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