Relate that image: A tool for finding related cultural heritage images

Museums,galleries, art centers, etc. are increasingly seeing the benefits of digitalizing their art work collections –and acting on it. The more visible benefits usually have to do with advertising, involving the citizens, or creating interactive tools that get people interested in coming to museums or buying art. With the availability of these increasingly large collections, analysis of art images has gained attention from researchers.This master thesis proposes a tool to recommend paintingsthat are similar to a given image of an artwork. We define different similarity measures that include criteria existent in the metadata associated with the digitized pictures (e.g. style, genre, artist, etc.), but also image content similarity. The work is more closely related to existing approaches on automatic classification of paintings, but also shares techniques with other areas such as image clustering. Our goal is to offer a tool that can enable creative uses, support the work of gallery / museum curators, help create interesting and interactive educational content, or create clusters of images as training sets for further learning and analysis algorithms

This is not an apple! Benefits and challenges of applying computer vision to museum collections

The application of computer vision on museum collection data is at an experimental stage with predictions that it will grow in significance and use in the coming years. This research, based on the analysis of five case studies and semi-structured interviews with museum professionals, examined the opportunities and challenges of these technologies, the resources and funding required, and the ethical implications that arise during these initiatives. The case studies examined in this paper are drawn from: The Metropolitan Museum of Art (USA), Princeton University Art Museum (USA), Museum of Modern Art (USA), Harvard Art Museums (USA), Science Museum Group (UK). The research findings highlight the possibilities of computer vision to offer new ways to analyze, describe and present museum collections. However, their actual implementation on digital products is currently very limited due to the lack of resources and the inaccuracies created by algorithms. This research adds to the rapidly evolving field of computer vision within the museum sector and provides recommendations to operationalize the usage of these technologies, increase the transparency on their application, create ethics playbooks to manage potential bias and collaborate across the museum sector

Archives, Access and Artificial Intelligence

Digital archives are transforming the Humanities and the Sciences. Digitized collections of newspapers and books have pushed scholars to develop new, data-rich methods. Born-digital archives are now better preserved and managed thanks to the development of open-access and commercial software. Digital Humanities have moved from the fringe to the center of academia. Yet, the path from the appraisal of records to their analysis is far from smooth. This book explores crossovers between various disciplines to improve the discoverability, accessibility, and use of born-digital archives and other cultural assets

Archives, Access and Artificial Intelligence: Working with Born-Digital and Digitized Archival Collections

Digital archives are transforming the Humanities and the Sciences. Digitized collections of newspapers and books have pushed scholars to develop new, data-rich methods. Born-digital archives are now better preserved and managed thanks to the development of open-access and commercial software. Digital Humanities have moved from the fringe to the center of academia. Yet, the path from the appraisal of records to their analysis is far from smooth. This book explores crossovers between various disciplines to improve the discoverability, accessibility, and use of born-digital archives and other cultural assets

Archives, Access and Artificial Intelligence

Digital archives are transforming the Humanities and the Sciences. Digitized collections of newspapers and books have pushed scholars to develop new, data-rich methods. Born-digital archives are now better preserved and managed thanks to the development of open-access and commercial software. Digital Humanities have moved from the fringe to the center of academia. Yet, the path from the appraisal of records to their analysis is far from smooth. This book explores crossovers between various disciplines to improve the discoverability, accessibility, and use of born-digital archives and other cultural assets

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

A Survey of Geometric Analysis in Cultural Heritage

We present a review of recent techniques for performing geometric analysis in cultural heritage (CH) applications. The survey is aimed at researchers in the areas of computer graphics, computer vision and CH computing, as well as to scholars and practitioners in the CH field. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives

Patterns of Discrimination: On Photographic Portraits as Documents of Truth in Automated Facial Recognition

Denne avhandlingen tar for seg fotografiers rolle i treningen av ansiktsgjenkjenningsalgoritmer, samt i selve den tekniske prosessen hvor ansikter analyseres. Gjennom en lesning av tre ulike kunstprosjekter som på ulike måter anvender eksisterende ansiktsgjenkjenningsteknologi til å problematisere denne praksisen, etablerer jeg hvordan ulike fordommer – særlig hva angår fotografiets status som objektiv representasjon av verden – påvirker systemenes evne til å analysere ansikter. De aktuelle prosjektene er ImageNet Roulette (2019) av Trevor Paglen og AI-forsker Kate Crawford, How do you see me? (2019) av Heather Dewey-Hagborg, og Spirit is a Bone (2013-15) av kunstner-duoen Broomberg & Chanarin. Problemstillingen som oppgaven forsøker å besvare er som følger: hva kan disse kunstprosjektene fortelle publikum om ansiktsgjenkjenningsteknologi som praksis, og hvilken rolle spiller digitalt fotografi som slike systemers bindeledd til den analoge verden «utenfor» dem selv? Som svar på dette tar avhandlingen for seg selve den tekniske arkitekturen og hvordan den legger føringer for ansiktsgjenkjenningssystemers operasjoner alt i designprosessen. I tillegg diskuteres ansiktsgjenkjenning fra et historisk perspektiv, hvor forsøk på å knytte juridisk identitet til kroppen gjennom fotografi spores helt tilbake til mediets oppfinnelse på 1800-tallet.Kunsthistorie mastergradsoppgaveKUN350MAHF-KU