19.4.5.6.3 Video Skimming

Chapter Contents (Back)
Video Skimming.

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

Li, Y.[Ying], Narayanan, S.S.[Shrikanth S.], Kuo, C.C.J.[C.C. Jay],
Adaptive Speaker Identification with Audio-Visual Cues for Movie Content Analysis,
PRL(25), No. 7, May 2004, pp. 777-791.
Elsevier DOI 0405
BibRef

Li, Y.[Ying], Narayanan, S.S.[Shrikanth S.], Kuo, C.C.J.[C.C. Jay],
Content-Based Movie Analysis and Indexing Based on Audio-Visual Cues,
CirSysVideo(14), No. 8, August 2004, pp. 1073-1085.
IEEE Abstract. 0409
BibRef
Earlier:
Movie Content Analysis, Indexing and Skimming Via Multimodal Information,
VideoMining03(Chapter 5). BibRef

Li, Y.[Ying], Kuo, C.C.J.[C.C. Jay],
A robust video scene extraction approach to movie content abstraction,
IJIST(13), No. 5, 2003, pp. 236-244.
DOI Link 0312
BibRef

Almeida, J.[Jurandy], 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


Ramos, W., Silva, M., Araujo, E., Marcolino, L.S., Nascimento, E.,
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

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

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

Vivekraj, V.K., Balasubramanian, R., Sen, D.,
Vector R-ordering based selection of segments for video skimming,
ICPR16(871-876)
IEEE DOI 1705
Color, Feature extraction, Motion segmentation, Organizations, Proposals, Visualization 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 .


Last update:Sep 14, 2020 at 15:32:18