Video Summaries through Mosaic-Based Shot and Scene Clustering

Video summaries provide a compact video representation preserving the essential activities of the original video, but the summaries may be confusing when mixing different activities together. Summaries Clustered methodology, showing similar activities simultaneously, enables to view much easier and more efficiently. However, it is very time consuming in generating summaries, especially in calculating motion distance and collision cost. To improve the efficiency of generating summaries, a parallel video synopsis generation algorithm is proposed based on GPGPU. The experiment result shows generation efficiency is improved greatly through GPU parallel computing. The acceleration radio can reach at 5.75 when data size is above 1600*960*30000.

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Using content models to build audio-video summaries

10.1117/12.333853 ◽

1998 ◽

Cited By ~ 4

Author(s):

Janne Saarela ◽

Bernard Merialdo

Keyword(s):

Video Summaries ◽

Audio Video

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Video Pulses: User-Based Modeling of Interesting Video Segments

Advances in Multimedia ◽

10.1155/2014/712589 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Markos Avlonitis ◽

Konstantinos Chorianopoulos

Keyword(s):

Information Seeking ◽

Video Retrieval ◽

Regions Of Interest ◽

Information Seeking Behavior ◽

Web Video ◽

User Activity ◽

Seeking Behavior ◽

Video Summaries ◽

Video Player ◽

The Web

We present a user-based method that detects regions of interest within a video in order to provide video skims and video summaries. Previous research in video retrieval has focused on content-based techniques, such as pattern recognition algorithms that attempt to understand the low-level features of a video. We are proposing a pulse modeling method, which makes sense of a web video by analyzing users'Replayinteractions with the video player. In particular, we have modeled the user information seeking behavior as a time series and the semantic regions as a discrete pulse of fixed width. Then, we have calculated the correlation coefficient between the dynamically detected pulses at the local maximums of the user activity signal and the pulse of reference. We have found that users'Replayactivity significantly matches the important segments in information-rich and visually complex videos, such as lecture, how-to, and documentary. The proposed signal processing of user activity is complementary to previous work in content-based video retrieval and provides an additional user-based dimension for modeling the semantics of a social video on the web.

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