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doctoral thesis

Making large art historical photo archives searchable

Seguin, Benoît Laurent Auguste  
2018

In recent years, museums, archives and other cultural institutions have initiated important programs to digitize their collections. Millions of artefacts (paintings, engravings, drawings, ancient photographs) are now represented in digital photographic format. Furthermore, through progress in standardization, a growing portion of these images are now available online, in an easily accessible manner. This thesis studies how such large-scale art history collection can be made searchable using new deep learning approaches for processing and comparing images. It takes as a case study the processing of the photo archive of the Foundation Giorgio Cini, where more than 300'000 images have been digitized. We demonstrate how a generic processing pipeline can reliably extract the visual and textual content of scanned images, opening up ways to efficiently digitize large photo-collections. Then, by leveraging an annotated graph of visual connections, a metric is learnt that allows clustering and searching through artwork reproductions independently of their medium, effectively solving a difficult problem of cross-domain image search. Finally, the thesis studies how a complex Web Interface allows users to perform different searches based on this metric. We also evaluate the process by which users can annotate elements of interest during their navigation to be added to the database, allowing the system to be trained further and give better results. By documenting a complete approach on how to go from a physical photo-archive to a state-of-the-art navigation system, this thesis paves the way for a global search engine across the world's photo archives.

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Type
doctoral thesis
DOI
10.5075/epfl-thesis-8857
Author(s)
Seguin, Benoît Laurent Auguste  
Advisors
Kaplan, Frédéric  
•
di Lenardo, Isabella  orcid-logo
Jury

Prof. Daniel Gatica-Perez (président) ; Prof. Frédéric Kaplan, Isabella di Lenardo (directeurs) ; Prof. Sabine Süsstrunk, Prof. Ondrej Chum, Prof. Bill Sherman (rapporteurs)

Date Issued

2018

Publisher

EPFL

Publisher place

Lausanne

Public defense year

2018-11-23

Thesis number

8857

Total of pages

169

Subjects

digitization

•

large image collections

•

visual search

•

deep learning

•

computer vision

•

archives

•

art history.

EPFL units
DHLAB  
School
DHI  
Doctoral School
EDIC  
Available on Infoscience
November 21, 2018
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/151526
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