Interactive Video Retrieval in the Age of Deep Learning - Detailed Evaluation of VBS 2019

Rossetto, Luca and Gasser, Ralph and Lokoč, Jakub and Bailer, Werner and Schoeffmann, Klaus and Muenzer, Bernd and Souček, Tomás and Nguyen, Phuong Anh and Bolettieri, Paolo and Leibetseder, Andreas and Vrochidis, Stefanos. (2021) Interactive Video Retrieval in the Age of Deep Learning - Detailed Evaluation of VBS 2019. IEEE Transactions on Multimedia, 23. pp. 243--256.

Full text not available from this repository.

Official URL: https://edoc.unibas.ch/81626/

Downloads: Statistics Overview


Despite the fact that automatic content analysis has made remarkable progress over the last decade - mainly due to significant advances in machine learning - interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenbanken (Schuldt)
UniBasel Contributors:Gasser, Ralph
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
Related URLs:
Identification Number:
Last Modified:12 Apr 2021 12:27
Deposited On:12 Apr 2021 12:27

Repository Staff Only: item control page