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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

17 Scopus Citations
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Author(s)

  • Luca Rossetto
  • Ralph Gasser
  • Jakub Lokoč
  • Werner Bailer
  • Klaus Schoeffmann
  • Bernd Muenzer
  • Tomáš Souček
  • Paolo Bolettieri
  • Andreas Leibetseder
  • Stefanos Vrochidis

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number9037125
Pages (from-to)243-256
Journal / PublicationIEEE Transactions on Multimedia
Volume23
Online published16 Mar 2020
Publication statusPublished - 2021

Abstract

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.

Research Area(s)

  • Interactive Video Retrieval, Video Browsing, Video Content Analysis, Content-based Retrieval, Evaluations

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Interactive Video Retrieval in the Age of Deep Learning - Detailed Evaluation of VBS 2019. / Rossetto, Luca; Gasser, Ralph; Lokoč, Jakub; Bailer, Werner; Schoeffmann, Klaus; Muenzer, Bernd; Souček, Tomáš; Nguyen, Phuong Anh; Bolettieri, Paolo; Leibetseder, Andreas; Vrochidis, Stefanos.

In: IEEE Transactions on Multimedia, Vol. 23, 9037125, 2021, p. 243-256.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review