An abstraction of low level video features for automatic retrievals of explosion scenes

Jongho Nang; Jinguk Jeong; Sungyong Park; Hojung Cha

doi:10.1007/3-540-36228-2_26

An abstraction of low level video features for automatic retrievals of explosion scenes

Jongho Nang, Jinguk Jeong, Sungyong Park, Hojung Cha

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper proposes an abstraction mechanism of the low-level digital video features for the automatic retrievals of the explosion scenes from the digital video library. In the proposed abstraction mechanism, the regional dominant colors of the key frame and the motion energy of the shot are defined as the primary low-level visual features of the shot for the explosion scene retrievals. The regional dominant colors of shot are selected by dividing its key frame image into several regions and extracting their regional dominant colors, and the motion energy of the shot is defined as the edge image differences between key frame and its neighboring frame. Upon the extensive experimental results, we could argue that the recall and precision of the proposed abstraction and detecting algorithm are about 0.8, and also found that they are not sensitive to the thresholds.

Original language	English
Title of host publication	Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings
Editors	Yung-Chang Chen, Long-Wen Chang, Chiou-Ting Hsu
Publisher	Springer Verlag
Pages	200-208
Number of pages	9
ISBN (Print)	3540002626, 9783540002628
DOIs	https://doi.org/10.1007/3-540-36228-2_26
Publication status	Published - 2002
Event	3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002 - Hsinchu, Taiwan, Province of China Duration: 2002 Dec 16 → 2002 Dec 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2532
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002
Country/Territory	Taiwan, Province of China
City	Hsinchu
Period	02/12/16 → 02/12/18

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-36228-2_26

Cite this

Nang, J., Jeong, J., Park, S., & Cha, H. (2002). An abstraction of low level video features for automatic retrievals of explosion scenes. In Y-C. Chen, L-W. Chang, & C-T. Hsu (Eds.), Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings (pp. 200-208). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2532). Springer Verlag. https://doi.org/10.1007/3-540-36228-2_26

Nang, Jongho ; Jeong, Jinguk ; Park, Sungyong et al. / An abstraction of low level video features for automatic retrievals of explosion scenes. Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings. editor / Yung-Chang Chen ; Long-Wen Chang ; Chiou-Ting Hsu. Springer Verlag, 2002. pp. 200-208 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An abstraction of low level video features for automatic retrievals of explosion scenes",

abstract = "This paper proposes an abstraction mechanism of the low-level digital video features for the automatic retrievals of the explosion scenes from the digital video library. In the proposed abstraction mechanism, the regional dominant colors of the key frame and the motion energy of the shot are defined as the primary low-level visual features of the shot for the explosion scene retrievals. The regional dominant colors of shot are selected by dividing its key frame image into several regions and extracting their regional dominant colors, and the motion energy of the shot is defined as the edge image differences between key frame and its neighboring frame. Upon the extensive experimental results, we could argue that the recall and precision of the proposed abstraction and detecting algorithm are about 0.8, and also found that they are not sensitive to the thresholds.",

author = "Jongho Nang and Jinguk Jeong and Sungyong Park and Hojung Cha",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002 ; Conference date: 16-12-2002 Through 18-12-2002",

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Nang, J, Jeong, J, Park, S & Cha, H 2002, An abstraction of low level video features for automatic retrievals of explosion scenes. in Y-C Chen, L-W Chang & C-T Hsu (eds), Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2532, Springer Verlag, pp. 200-208, 3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002, Hsinchu, Taiwan, Province of China, 02/12/16. https://doi.org/10.1007/3-540-36228-2_26

An abstraction of low level video features for automatic retrievals of explosion scenes. / Nang, Jongho; Jeong, Jinguk; Park, Sungyong et al.

Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings. ed. / Yung-Chang Chen; Long-Wen Chang; Chiou-Ting Hsu. Springer Verlag, 2002. p. 200-208 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2532).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Nang, Jongho

AU - Jeong, Jinguk

AU - Park, Sungyong

AU - Cha, Hojung

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2002.

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N2 - This paper proposes an abstraction mechanism of the low-level digital video features for the automatic retrievals of the explosion scenes from the digital video library. In the proposed abstraction mechanism, the regional dominant colors of the key frame and the motion energy of the shot are defined as the primary low-level visual features of the shot for the explosion scene retrievals. The regional dominant colors of shot are selected by dividing its key frame image into several regions and extracting their regional dominant colors, and the motion energy of the shot is defined as the edge image differences between key frame and its neighboring frame. Upon the extensive experimental results, we could argue that the recall and precision of the proposed abstraction and detecting algorithm are about 0.8, and also found that they are not sensitive to the thresholds.

AB - This paper proposes an abstraction mechanism of the low-level digital video features for the automatic retrievals of the explosion scenes from the digital video library. In the proposed abstraction mechanism, the regional dominant colors of the key frame and the motion energy of the shot are defined as the primary low-level visual features of the shot for the explosion scene retrievals. The regional dominant colors of shot are selected by dividing its key frame image into several regions and extracting their regional dominant colors, and the motion energy of the shot is defined as the edge image differences between key frame and its neighboring frame. Upon the extensive experimental results, we could argue that the recall and precision of the proposed abstraction and detecting algorithm are about 0.8, and also found that they are not sensitive to the thresholds.

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ER -

Nang J, Jeong J, Park S, Cha H. An abstraction of low level video features for automatic retrievals of explosion scenes. In Chen Y-C, Chang L-W, Hsu C-T, editors, Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings. Springer Verlag. 2002. p. 200-208. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-36228-2_26

An abstraction of low level video features for automatic retrievals of explosion scenes

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All Science Journal Classification (ASJC) codes

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