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Efficient video indexing scheme for content-based retrieval

Published: 01 December 1999 Publication History

Abstract

Extracting a small number of key frames that can abstract the content of video is very important for efficient browsing and retrieval in video databases. In this paper, the key frame extraction problem is considered from a set-theoretic point of view, and systematic algorithms are derived to find a compact set of key frames that can represent a video segment for a given degree of fidelity. The proposed extraction algorithms can be hierarchically applied to obtain a tree-structured key frame hierarchy that is a multilevel abstract of the video. The key frame hierarchy enables an efficient content-based retrieval by using the depth-first search scheme with pruning., Intensive experiments on a variety of video sequences are presented to demonstrate the improved performance of the proposed algorithms over the existing approaches

Cited By

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  • (2023)Video summarization using deep learning techniques: a detailed analysis and investigationArtificial Intelligence Review10.1007/s10462-023-10444-056:11(12347-12385)Online publication date: 15-Mar-2023
  • (2020)Performance over RandomProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413632(1056-1064)Online publication date: 12-Oct-2020
  • (2019)From local to global key-frame extraction based on important scenes using SVD of centrist featuresMultimedia Tools and Applications10.1007/s11042-018-6274-078:2(1441-1456)Online publication date: 1-Jan-2019
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cover image IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology  Volume 9, Issue 8
December 1999
184 pages

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IEEE Press

Publication History

Published: 01 December 1999

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Cited By

View all
  • (2023)Video summarization using deep learning techniques: a detailed analysis and investigationArtificial Intelligence Review10.1007/s10462-023-10444-056:11(12347-12385)Online publication date: 15-Mar-2023
  • (2020)Performance over RandomProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413632(1056-1064)Online publication date: 12-Oct-2020
  • (2019)From local to global key-frame extraction based on important scenes using SVD of centrist featuresMultimedia Tools and Applications10.1007/s11042-018-6274-078:2(1441-1456)Online publication date: 1-Jan-2019
  • (2018)Effective Video Shot Boundary Detection and Keyframe Selection using Soft Computing TechniquesInternational Journal of Computer Vision and Image Processing10.4018/IJCVIP.20180401028:2(27-48)Online publication date: 1-Apr-2018
  • (2018)Eratosthenes sieve based key-frame extraction technique for event summarization in videosMultimedia Tools and Applications10.1007/s11042-017-4642-977:6(7383-7404)Online publication date: 1-Mar-2018
  • (2018)Key-frame selection for automatic summarization of surveillance videosMachine Vision and Applications10.1007/s00138-018-0954-729:7(1101-1117)Online publication date: 1-Oct-2018
  • (2017)Motion-State-Adaptive Video Summarization via Spatiotemporal AnalysisIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2016.253963827:6(1340-1352)Online publication date: 2-Jun-2017
  • (2017)A novel clustering method for static video summarizationMultimedia Tools and Applications10.1007/s11042-016-3569-x76:7(9625-9641)Online publication date: 1-Apr-2017
  • (2017)Robust handwriting extraction and lecture video summarizationMultimedia Tools and Applications10.1007/s11042-016-3353-y76:5(7067-7085)Online publication date: 1-Mar-2017
  • (2017)Unsupervised, efficient and scalable key-frame selection for automatic summarization of surveillance videosMultimedia Tools and Applications10.1007/s11042-016-3263-z76:5(6309-6331)Online publication date: 1-Mar-2017
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