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Keyframe-based video summarization with human in the loop

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

Details

Original languageEnglish
Title of host publicationVISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
PublisherSCITEPRESS
Pages287-296
Number of pages10
Volume4
ISBN (Electronic)9789897582905
DOIs
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventINTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS -
Duration: 1 Jan 1900 → …

Conference

ConferenceINTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS
Period1/01/00 → …

Abstract

In this work, we focus on the popular keyframe-based approach for video summarization. Keyframes represent important and diverse content of an input video and a summary is generated by temporally expanding the keyframes to key shots which are merged to a continuous dynamic video summary. In our approach, keyframes are selected from scenes that represent semantically similar content. For scene detection, we propose a simple yet effective dynamic extension of a video Bag-of-Words (BoW) method which provides over segmentation (high recall) for keyframe selection. For keyframe selection, we investigate two effective approaches: local region descriptors (visual content) and optical flow descriptors (motion content). We provide several interesting findings. 1) While scenes (visually similar content) can be effectively detected by region descriptors, optical flow (motion changes) provides better keyframes. 2) However, the suitable parameters of the motion descriptor based keyframe selection vary from one video to another and average performances remain low. To avoid more complex processing, we introduce a human-in-the-loop step where user selects keyframes produced by the three best methods. 3) Our human assisted and learning-free method achieves superior accuracy to learning-based methods and for many videos is on par with average human accuracy.

Keywords

  • Optical flow descriptors, Region descriptors, Video summarization, Visual bag-of-words

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