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.