Clustering for the Analysis and Enrichment of Corpus of Images for the Spatio-temporal Monitoring of Restoration Sites

Laura Willot (1, 2, 3, 4) , Dan Vodislav (2) , Valerie Gouet-Brunet (4) , Livio De Luca (3) , Adeline Manuel (3)

1 Fondation des Sciences du Patrimoine
2 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
3 MAP - Modèles et simulations pour l'Architecture et le Patrimoine
4 LaSTIG - Laboratoire sciences et technologies de l'information géographique

After the fire that took place in the cathedral Notre-Dame de Paris, a Scientific research group composed of 9 Working Groups was set up to study the building. With the aim of creating a digital ecosystem for spatio-temporal monitoring of a restoration site, efforts have been put to develop methodologies for data management and enrichment. In line with this approach, this work focuses on the exploration, analysis and enrichment of a corpus of images and annotations of the cathedral, with regards to their temporal, spatial and semantic aspects, in addition to their visual content. Using prevailing clustering methods focusing on different criteria, we obtained several clusters of images from which new information can be inferred. By exploring these complementary aspects, we identified similarity links between the images. Further work will focus on multimodal analyses of the corpus.

Keywords: Data Enrichment; Clustering; Cultural Heritage; Semantic Analysis; Multimodality; Data Enrichment

Domains: Image Processing [eess.IV]; Cultural heritage and museology

Laura Willot, Dan Vodislav, Valerie Gouet-Brunet, Livio De Luca, Adeline Manuel. Clustering for the Analysis and Enrichment of Corpus of Images for the Spatio-temporal Monitoring of Restoration Sites. 5th Workshop on AnalySis, Understanding and ProMotion of HeritAge Contents (SUMAC '23), ACM Multimedia 2023, Nov 2023, Ottawa, Canada. pp.39-47, ⟨10.1145/3607542.3617353⟩. ⟨hal-04283568⟩