Although the majority of Digital Humanities scholars still focus on textual analysis, we see an increasing number of studies using digitised visual sources and taking the first steps in the field of ‘Visual Big Data’ (Ordelman et al, 2014). Scholars have increasingly used both large-scale digitised visual datasets as well as computational methods to analyse these datasets in new ways, see for example the work of Lev Manovich on Manga comics (2012), Time covers, and selfies (Manovich & Tifentale 2015) or the recent work of Lenardo et al (2016) on visual pattern discovery in large databases of paintings as presented at the DH 2016 conference. Others focus on the identification of finding recurring images in visual collections. Terras & Kirton (2013) analyse the reuse of digital images of cultural and heritage material on the internet while Resig (2014) and Reside (2015) follow a similar approach using a closed dataset.
A similar trend can be observed at the ADHO Digital Humanities Conferences. In 2014 the special Interest group Audiovisual Materials in Digital Humanities was founded and at the 2015 and 2016 conferences, several papers were presented using (audio) visual sources (see e.g. Kleppe 2015, Lenardo et al. 2016, Lincoln 2016). Based on an analysis of the submissions for the 2017 conference, Scott Weingart observes that an increasing number of papers are based on research concerning non-textual sources (Weingart 2017). The use of non-textual sources challenges established research practices of Digital Humanities, leading to new research questions and the need for transformation of existing approaches, and the development of new methodologies. For example, how can we analyse the characteristics of images in large visual datasets? Some of these questions have also been raised in the analogue era. See e.g. the work of Barry Salt (1974) on the characteristics of opening shots of twentieth-century films, Scott McCloud (1994) on the visual language of Japanese manga and comics, or Peter Burke’s take on using visual materials as historical evidence (2001).
Due to the digital turn, we now see the application of new techniques to answer similar and new types of research questions. One of the dominant techniques to analyse large-scale visual datasets is computer vision: a field that deals with how computers can generate a high-level of understanding of visual material (Smeulders 2000).This field has been seeing unprecedented improvements as part of the “Deep Learning Revolution” since the first deep Convolutional Neural Network (Krizhevsky 2012), unleashing new possibilities.
This workshop will take place during the Digital Humanities 2017 conference and will focus on how computer vision can be applied within the realm of Audiovisual Materials in Digital Humanities. During the workshop, attendees will both present (ongoing) work on applying computer vision and experiment with computer vision in their own work in a hands-on session.
The workshop will consist of four parts:
- A keynote by Lindsay King (Associate Director for Access and Research Services Robert B. Haas Family Arts Library, Yale University) & Peter Leonard (Director Digital Humanities Lab, Yale University Library) & on “Processing Pixels: Towards Visual Culture Computation”
- Paper presentations with results and ongoing work on applying computer vision in DH research projects.
- A hands-on session in which participants will be able to experiment with open source Computer Vision tools. This session will be led by Benoit Seguin of École Polytechnique Fédérale de Lausanne, (EPFL).
- Lightning Talks allowing participants to share their ideas, projects or ongoing work in a short presentation of two minutes. Interested researchers who want to present work will be able to submit their idea in a later stage.
Invitation to submit abstract
The organisers of the workshop now invite scholars to present their work on one of the following possible topics to present during the paper presentations session:
- Results or ongoing work and research in which computer vision was applied and what opportunities and challenges were faced
- How can cooperation between Humanities researchers and computer vision experts be improved so both can benefit of each other’s expertise?
- What kind of large publicly available annotated datasets are of use for Digital Humanities researchers?
Submission of proposals
Submissions should include the following:
- General abstract (should not exceed 500 words) outlining your work, research question, results and possible challenges you faced.
- Contact info and a short description of research interests of the authors.
To submit a proposal, please send a docx or pdf file to firstname.lastname@example.org before May 31 2017. Accepted abstracts will be published on the website of the AVinDH Special Interest Group.
Participants and presenters are expected to register for the DH2017 conference and are expected to pay a small additional fee to participate in workshops.
All proposals will be reviewed by members of the Program Committee which will be chaired by Prof. Dr. Franciska de Jong, Erasmus University Rotterdam / CLARIN-ERIC. The following persons agreed to be part of the program committee. More members will be added soon:
Prof. dr. Andrew Zisserman – University of Oxford, United Kingdom
dr. Doug Reside – New York Public Library, USA
Hans Brandhorst – Editor Iconclass and co-founder Arkyves, The Netherlands
Dr. Jakob Verbeek – INRIA, France
Dr. Marianne Ping Huang – University of Aarhus, Denmark
Prof. dr. Remco Veltkamp – Utrecht University, The Netherlands
Prof. dr. Tinne Tuytelaar – KU Leuven, Belgium
Important Dates (to be confirmed)
Deadline: 31 May 23:59 2017
Date for notification: 15 June 2017
Workshop: Monday 7 August 2017
Organisation & Contact
The workshop is organised by the following members of the organising committee:
Martijn Kleppe, National Library of the Netherlands (KB)
Matthew Lincoln, Getty Research Institute
Melvin Wevers, Utrecht University (UU)
Mark Williams, Dartmouth College
Benoit Seguin, École Polytechnique Fédérale de Lausanne (EPFL)
Thomas Smits, Radboud University (RU)
Questions & more information
For questions related to the call or workshop, please do not hesitate to contact Martijn Kleppe via email@example.com
Burke, P. (2001). Eyewitnessing. The uses of images as historical evidence. London: Cornell University Press
Kleppe, M. (2015) Tracing the afterlife of iconic photographs using IPTC. Digital Humanities 2015, 29 Juni – 3 Juli 2015, Sydney
Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012). ImageNet classification with deep convolutionnal neural network
di Lenardo, I., Seguin, B., Kaplan, F. (2016). Visual Patterns Discovery in Large Databases of Paintings. In Digital Humanities 2016: Conference Abstracts. Jagiellonian University & Pedagogical University, Kraków, pp. 169-172. http://dh2016.adho.org/abstracts/348
Lincoln, M. (2016). If Paintings were Plants: Measuring Genre Diversity in Seventeenth-Century Dutch Painting and Printmaking. In Digital Humanities 2016: Conference Abstracts. Jagiellonian University & Pedagogical University, Kraków, pp. 256-259. http://dh2016.adho.org/abstracts/133
Manovich, L. (2012). How to compare one million Images? In Berry, D. M., Understanding Digital Humanities, pp. 249-78.
Manovich. L. and Tifentale, A. (2015). Selfiecity: Exploring Photography and Self-Fashioning in Social Media. In Berry, David M., Dieter, M. (eds), Postdigital Aesthetics: Art, Computation and Design, pp. 109-22.
McCLoud, S . (1994). Understanding Comics: The Invisible Art. New York: HarperPerenn
Ordelman, R., Kemman, M., Kleppe, M., de Jong, F., Scagliola, S. (2014) Sound and (moving) Images in Focus – How to integrate Audiovisual Material in Digital Humanities Research. Digital Humanities 2014 http://dharchive.org/paper/DH2014/Workshops-914.xml
Reser, G. and Bauman, J. (2012). The Past, Present, and Future of Embedded Metadata for the Long-Term Maintenance of and Access to Digital Image Files. International Journal of Digital Library Systems (IJDLS), 3(1): 53-64.
Reside, D. (2014). Using Computer Vision to Improve Image Metadata. Digital Humanities 2014. http://dharchive.org/paper/DH2014/Paper-294.xml
Salt, B. (1974). The Statistical Style Analysis of Motion Pictures. Film Quarterly, 28(1): 13-22.
Smeulders, A. W., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349-138
Weingart, S. (2017), Submission to DH2017 (pt. 1) http://scottbot.net/submissions-to-dh2017-pt-1/
Terras, M. M. and Kirton, I. (2013). Where do images of art go once they go online? A Reverse Image Lookup study to assess the dissemination of digitized cultural heritage. Selected papers from Museums and the Web North America, pp. 237–48. http://mw2013.museumsandtheweb.com/paper/where-do-images-of-art-go-once-they-go-online-a-reverse-image-lookup-study-to-assess-the-dissemination-of-digitized-cultural-heritage/