In this paper, we present a novel approach that combines density-based clustering and graph modeling
to create a scalable viewing application for the exploration of similarity patterns in news videos. Unlike
most existing video analysis tools that focus on individual videos, our approach allows for an overview of
a larger collection of videos, which can be further examined based on their connections or communities.
By utilizing scalable reading, specific subgraphs can be selected from the overview and their respective
clusters can be explored in more detail on the video frame level
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:92507 |
| Date | 04 July 2024 |
| Creators | Ruth, Nicolas, Liebl, Bernhard, Burghardt, Manuel |
| Publisher | CEUR-WS.org |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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