Deep Learning-based Concept Detection in vitrivr at the Video Browser Showdown 2019 - Final Notes

This paper presents an after-the-fact summary of the participation of the vitrivr system to the 2019 Video Browser Showdown. Analogously to last year's report, the focus of this paper lies on additions made since the original publication and the system's performance during the competition

Competitive Video Retrieval with vitrivr

This paper presents the competitive video retrieval capabilities of vitrivr.  The vitrivr stack is the continuation of the IMOTION system which participated to the Video Browser Showdown competitions since 2015. The primary focus of vitrivr and its participation in this competition is to simplify and generalize the system's individual components, making them easier to deploy and use. The entire vitrivr stack is made available as open source software

Enhanced Retrieval and Browsing in the IMOTION System

This paper presents the IMOTION system in its third version. While still focusing on sketch-based retrieval, we improved upon the semantic retrieval capabilities introduced in the previous version by adding more detectors and improving the interface for semantic query specication. In addition to previous year's system, we increase the role of features obtained from Deep Neural Networks in three areas: semantic class labels for more entry-level concepts, hidden layer activation vectors for query-by-example and 2D semantic similarity results display. The new graph-based result navigation interface further enriches the system's browsing capabilities. The updated database storage system ADAMpro designed from the ground up for large scale multimedia applications ensures the scalability to steadily growing collections