AI-Driven Classification of a Design Photographic Archive
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The 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.
Description
@inproceedings{10.2312:gch.20241258,
booktitle = {Eurographics Workshop on Graphics and Cultural Heritage},
editor = {Corsini, Massimiliano and Ferdani, Daniele and Kuijper, Arjan and Kutlu, Hasan},
title = {{AI-Driven Classification of a Design Photographic Archive}},
author = {Rodriguez Echavarria, Karina and Samaroudi, Myrsini},
year = {2024},
publisher = {The Eurographics Association},
ISSN = {2312-6124},
ISBN = {978-3-03868-248-6},
DOI = {10.2312/gch.20241258}
}