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Hierarchical video summarization in reference subspace

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Hierarchical video summarization in reference subspace. / Jiang, Richard; Sadka, Abdul; Crookes, Danny.
In: IEEE Transactions on Consumer Electronics, Vol. 55, No. 3, 08.2009, p. 1551 - 1557.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Jiang, R, Sadka, A & Crookes, D 2009, 'Hierarchical video summarization in reference subspace', IEEE Transactions on Consumer Electronics, vol. 55, no. 3, pp. 1551 - 1557. https://doi.org/10.1109/tce.2009.5278026

APA

Jiang, R., Sadka, A., & Crookes, D. (2009). Hierarchical video summarization in reference subspace. IEEE Transactions on Consumer Electronics, 55(3), 1551 - 1557. https://doi.org/10.1109/tce.2009.5278026

Vancouver

Jiang R, Sadka A, Crookes D. Hierarchical video summarization in reference subspace. IEEE Transactions on Consumer Electronics. 2009 Aug;55(3):1551 - 1557. doi: 10.1109/tce.2009.5278026

Author

Jiang, Richard ; Sadka, Abdul ; Crookes, Danny. / Hierarchical video summarization in reference subspace. In: IEEE Transactions on Consumer Electronics. 2009 ; Vol. 55, No. 3. pp. 1551 - 1557.

Bibtex

@article{64bca5e9251d423eb09f356865f66b76,
title = "Hierarchical video summarization in reference subspace",
abstract = "In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using k-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently.",
author = "Richard Jiang and Abdul Sadka and Danny Crookes",
year = "2009",
month = aug,
doi = "10.1109/tce.2009.5278026",
language = "English",
volume = "55",
pages = "1551 -- 1557",
journal = "IEEE Transactions on Consumer Electronics",
issn = "0098-3063",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Hierarchical video summarization in reference subspace

AU - Jiang, Richard

AU - Sadka, Abdul

AU - Crookes, Danny

PY - 2009/8

Y1 - 2009/8

N2 - In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using k-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently.

AB - In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using k-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently.

U2 - 10.1109/tce.2009.5278026

DO - 10.1109/tce.2009.5278026

M3 - Journal article

VL - 55

SP - 1551

EP - 1557

JO - IEEE Transactions on Consumer Electronics

JF - IEEE Transactions on Consumer Electronics

SN - 0098-3063

IS - 3

ER -