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Rethinking the Evaluation of Video Summaries

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
Pages7588-7596
Number of pages9
ISBN (Electronic)978-1-7281-3293-8
ISBN (Print)978-1-7281-3294-5
DOIs
Publication statusPublished - Jun 2019
Publication typeA4 Article in a conference publication
EventIEEE/CVF Conference on Computer Vision and Pattern Recognition -
Duration: 1 Jan 2000 → …

Publication series

NameIEEE/CVF Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR
Period1/01/00 → …

Abstract

Video summarization is a technique to create a short skim of the original video while preserving the main stories/content. There exists a substantial interest in automatizing this process due to the rapid growth of the available material. The recent progress has been facilitated by public benchmark datasets, which enable easy and fair comparison of methods. Currently the established evaluation protocol is to compare the generated summary with respect to a set of reference summaries provided by the dataset. In this paper, we will provide in-depth assessment of this pipeline using two popular benchmark datasets. Surprisingly, we observe that randomly generated summaries achieve comparable or better performance to the state-of-the-art. In some cases, the random summaries outperform even the human generated summaries in leave-one-out experiments. Moreover, it turns out that the video segmentation, which is often considered as a fixed pre-processing method, has the most significant impact on the performance measure. Based on our observations, we propose alternative approaches for assessing the importance scores as well as an intuitive visualization of correlation between the estimated scoring and human annotations.

Keywords

  • Datasets and Evaluation, Vision Applications and Systems

Publication forum classification

Field of science, Statistics Finland