AI-Driven Classification of a Design Photographic Archive

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dc.contributor.authorRodriguez Echavarria, Karinaen_US
dc.contributor.authorSamaroudi, Myrsinien_US
dc.contributor.editorCorsini, Massimilianoen_US
dc.contributor.editorFerdani, Danieleen_US
dc.contributor.editorKuijper, Arjanen_US
dc.contributor.editorKutlu, Hasanen_US
dc.date.accessioned2024-09-15T09:57:15Z
dc.date.available2024-09-15T09:57:15Z
dc.date.issued2024
dc.description.abstractThe paper presents a workflow for deploying an Artificial Intelligence (AI) classification of a previously unclassified photographic collection, the Design Archive's glass plate negatives. This involved fine-tuning the DinoV2 self-supervised image retrieval system with a domain-expert taxonomy to classify approximately 10K images within 40 classes. As such, it addresses challenges relevant to the curation, analysis and discovery of large-scale visual collections. A 3D visualisation was implemented for users to access the outputs presenting images as data points using the Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) to project the embeddings of the neural network. The paper demonstrates the advantages of this approach and reflects how users can participate in the AI processes making them more transparent and trustable.en_US
dc.description.sectionheadersAI-image Classification, Cognitive Studies, Virtual Reality and how to fuse Heterogeneous CH Data
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.identifier.doi10.2312/gch.20241258
dc.identifier.isbn978-3-03868-248-6
dc.identifier.issn2312-6124
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/gch.20241258
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/gch20241258
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectKeywords: Information Systems, Cultural Heritage Collections, Information Discovery CCS Concepts: Information systems → Information extraction; Computing methodologies → Graphics systems and interfaces; Applied computing → Fine arts; Human-centered computing → Visualization techniques
dc.subjectInformation Systems
dc.subjectCultural Heritage Collections
dc.subjectInformation Discovery CCS Concepts
dc.subjectInformation systems → Information extraction
dc.subjectComputing methodologies → Graphics systems and interfaces
dc.subjectApplied computing → Fine arts
dc.subjectHuman centered computing → Visualization techniques
dc.titleAI-Driven Classification of a Design Photographic Archiveen_US
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GCH 2024 - Eurographics Workshop on Graphics and Cultural Heritage