Mauldin, M.L.[Michael L.],
Smith, M.A.[Michael A.],
Stevens, S.M.[Scott M.],
Wactlar, H.D.[Howard D.],
Christel, M.G.[Michael G.],
Reddy, D.R.[D. Raj],
System and method for skimming digital audio/video data,
US_Patent5,664,227, Sep 2, 1997
WWW Link.
BibRef
9709
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Leite, N.J.[Neucimar J.],
da Silva Torres, R.[Ricardo],
Online video summarization on compressed domain,
JVCIR(24), No. 6, August 2013, pp. 729-738.
Elsevier DOI
1306
Video abstraction; Video summary; Video skimming; Compressed
domain; Progressive generation; Online processing; TRECVID 2007; BBC
rushes summarization
BibRef
Sreeja, M.U.,
Kovoor, B.C.[Binsu C.],
Towards genre-specific frameworks for video summarisation: A survey,
JVCIR(62), 2019, pp. 340-358.
Elsevier DOI
1908
Video summarisation, Video summary, Genre-specific, Skim, Keyframe
BibRef
Vivekraj, V.K.,
Sen, D.[Debashis],
Raman, B.[Balasubramanian],
Video Skimming: Taxonomy and Comprehensive Survey,
Surveys(52), No. 5, October 2019, pp. Article No 106.
DOI Link
1912
Survey, Video Skimming.
BibRef
Kumar, K.[Krishan],
EVS-DK: Event video skimming using deep keyframe,
JVCIR(58), 2019, pp. 345-352.
Elsevier DOI
1901
Clustering, Deep learning, Event summarization,
Highly connected subgraph, Key-frames, Video, Graph
BibRef
Silva, M.M.[Michel Melo],
Ramos, W.L.S.[Washington Luis Souza],
Campos, M.F.M.[Mario Fernando Montenegro],
Nascimento, E.R.[Erickson Rangel],
A Sparse Sampling-Based Framework for Semantic Fast-Forward of
First-Person Videos,
PAMI(43), No. 4, April 2021, pp. 1438-1444.
IEEE DOI
2103
Videos, Semantics, Visualization, Acceleration, Cameras, Encoding,
Pattern analysis, First-person video, fast-forward,
minimum sparse reconstruction problem
BibRef
Silva, M.M.[Michel Melo],
Ramos, W.L.S.[Washington Luis Souza],
Ferreira, J.P.K.[Joao Pedro Klock],
Chamone, F.,
Campos, M.F.M.[Mario Fernando Montenegro],
Nascimento, E.R.[Erickson Rangel],
A Weighted Sparse Sampling and Smoothing Frame Transition Approach
for Semantic Fast-Forward First-Person Videos,
CVPR18(2383-2392)
IEEE DOI
1812
Videos, Semantics, Cameras, Visualization, Smoothing methods,
Dictionaries, Computational modeling
BibRef
Silva, M.M.[Michel Melo],
Ramos, W.L.S.[Washington Luis Souza],
Ferreira, J.P.K.[Joao Pedro Klock],
Campos, M.F.M.[Mario Fernando Montenegro],
Nascimento, E.R.[Erickson Rangel],
Towards Semantic Fast-Forward and Stabilized Egocentric Videos,
Egocentric16(I: 557-571).
Springer DOI
1611
BibRef
And: A2, A1, A4, A5, Only:
Fast-forward video based on semantic extraction,
ICIP16(3334-3338)
IEEE DOI
1610
Biomedical monitoring. To edit egocentric videos into something useful.
BibRef
Ramos, W.L.S.[Washington Luis Souza],
Silva, M.M.[Michel Melo],
Araujo, E.,
Marcolino, L.S.,
Nascimento, E.R.[Erickson Rangel],
Straight to the Point: Fast-Forwarding Videos via Reinforcement
Learning Using Textual Data,
CVPR20(10928-10937)
IEEE DOI
2008
Videos, Visualization, Task analysis, Semantics,
Learning (artificial intelligence), Acceleration, Training
BibRef
Sun, X.Y.[Xiao-Yang],
Wang, H.L.[Han-Li],
He, B.[Bin],
MABAN: Multi-Agent Boundary-Aware Network for Natural Language Moment
Retrieval,
IP(30), 2021, pp. 5589-5599.
IEEE DOI
2106
Videos, Reinforcement learning, Task analysis, Semantics,
Natural languages, Visualization, Sun, temporal reasoning
BibRef
Lan, S.[Shuyue],
Wang, Z.[Zhilu],
Wei, E.[Ermin],
Roy-Chowdhury, A.K.[Amit K.],
Zhu, Q.[Qi],
Collaborative Multi-Agent Video Fast-Forwarding,
MultMed(26), 2024, pp. 1041-1054.
IEEE DOI
2402
Cameras, Streaming media, Robot vision systems,
Reinforcement learning, Multi-agent systems, Collaboration
BibRef
Lan, S.,
Panda, R.,
Zhu, Q.,
Roy-Chowdhury, A.K.,
FFNet: Video Fast-Forwarding via Reinforcement Learning,
CVPR18(6771-6780)
IEEE DOI
1812
Streaming media, Real-time systems, Markov processes, Time factors
BibRef
Christel, M.G.[Michael G.],
Lin, W.H.[Wei-Hao],
Maher, B.[Bryan],
Evaluating audio skimming and frame rate acceleration for summarizing
BBC rushes,
CIVR08(407-416).
0807
BibRef
Sundaram, H.[Hari],
Chang, S.F.[Shih-Fu],
Video skims: taxonomies and an optimal generation framework,
ICIP02(II: 21-24).
IEEE DOI
0210
BibRef
Earlier:
Constrained Utility Maximizations for Generating Visual Skims,
CBAIVL01(124).
IEEE DOI
0110
BibRef
Ma, Y.F.[Yu-Fei],
Zbang, H.J.,
A model of motion attention for video skimming,
ICIP02(I: 129-132).
IEEE DOI
0210
BibRef
di Lecce, V.,
Dimauro, G.,
Guerriero, A.,
Impedovo, S.,
Pirlo, G.,
Salzo, A.,
Image basic features indexing techniques for video skimming,
CIAP99(715-720).
IEEE DOI
9909
BibRef
Smith, M.A.[Michael A.],
Kanade, T.[Takeo],
Video Skimming and Characterization through the Combination of
Image and Language Understanding Techniques,
CVPR97(775-781).
IEEE DOI
9704
BibRef
And:
DARPA97(357-366).
BibRef
And:
CMU-CS-TR-97-111, February 1997.
Language from audio produce a skim.
PS File.
BibRef
Smith, M.A.[Michael A.],
Kanade, T.[Takeo],
Video Skimming for Quick Browsing based on Audio and
Image Characterization,
CMU-CS-TR-95-186, July 1995.
PS File.
BibRef
9507
Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Video Understanding .