vitrivr-explore: Guided Multimedia Collection Exploration for Ad-hoc Video Search
vitrivr is an open-source system for indexing and retrieving multimedia data based on its content and it has been a fixture at the Video Browser Showdown (VBS) in the past years. While vitrivr has proven to be competitive in content-based retrieval due to the many different query modes it supports, its functionality is rather limited when it comes to exploring a collection or searching result sets based on content. In this demonstration, we present vitrivr-explore, an extension to the vitrivr stack that allows to explore multimedia collections using relevance feedback. For this, our implementation integrates into the existing features of vitrivr and exploits self-organizing maps. Users initialize the exploration either with a query or just pick examples from a collection while browsing. Exploration can be based on a mixture of semantic and visual features. We describe our architecture and implementation and present preliminary results of vitrivr-explore in a competitive VBS-like evaluation. These results show that vitrivr-explore is competitive for Ad-hoc Video Search (AVS) tasks, even without user initialization. In the demo, users will be able to explore the V3C1 collection using both traditional retrieval methods in vitrivr and guided exploration with self-organizing maps in vitrivr-explore.