Content-Based Exploration of Archival Images Using Neural Networks
| dc.contributor.author | Adewoye, Tobi | |
| dc.contributor.author | Han, Xiao | |
| dc.contributor.author | Ruest, Nick | |
| dc.contributor.author | Milligan, Ian | |
| dc.contributor.author | Fritz, Samantha | |
| dc.contributor.author | Lin, Jimmy | |
| dc.date.accessioned | 2020-05-26T12:42:52Z | |
| dc.date.available | 2020-05-26T12:42:52Z | |
| dc.date.issued | 2020-08 | |
| dc.description.abstract | We present DAIRE (Deep Archival Image Retrieval Engine), an image exploration tool based on latent representations derived from neural networks, which allows scholars to "query" using an image of interest to rapidly find related images within a web archive. This work represents one part of our broader effort to move away from text-centric analyses of web archives and scholarly tools that are direct reflections of methods for accessing the live web. This short piece describes the implementation of our system and a case study on a subset of the GeoCities web archive. | en_US |
| dc.description.sponsorship | This research was supported in part by the Andrew W. Mellon Foundation and the Social Sciences and Humanities Research Council of Canada. | en_US |
| dc.identifier.uri | https://doi.org/10.1145/3383583.3398577 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10315/37504 | |
| dc.language.iso | en | en_US |
| dc.publisher | ACM/IEEE | en_US |
| dc.relation.ispartofseries | Joint Conference on Digital Libraries;2020 | |
| dc.rights.article | https://doi.org/10.1145/3383583.3398577 | en_US |
| dc.subject | web archives | en_US |
| dc.subject | image retrieval | en_US |
| dc.subject | neural networks | en_US |
| dc.subject | information retrieval | en_US |
| dc.subject | Information systems~Image search | |
| dc.subject | Applied computing~Digital libraries and archives | |
| dc.subject | image similarity | |
| dc.subject | image browsing | |
| dc.subject | GeoCities | |
| dc.title | Content-Based Exploration of Archival Images Using Neural Networks | en_US |
| dc.type | Article | en_US |