Video Clip Growth: A General Algorithm for Multi-view Video Summarization

Pan, Gang; Qu, Xingming; Lv, Lina; Guo, Shuai; Sun, Di

doi:10.1007/978-3-030-00764-5_11

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most related work to ours is the work done by Michael Gygli in [26], where superframes defined with a definition of consecutive frames are aligned with positions of a video that are appropriate for a video cut. Inspired by superframe [26], superpixel [27,28], and video clip growth [29], our algorithm can be summarized as below: clustering is first used to oversegment video into video clips. Intuitively, frames are not isolated and all frames in a short period of time should have a high degree of similarity.…”

Section: Introductionmentioning

confidence: 99%

A bottom-up summarization algorithm for videos in the wild

Pan

Zheng

Zhang

et al. 2019

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

Video summarization aims to provide a compact video representation while preserving the essential activities of the original video. Most existing video summarization approaches relay on identifying important frames and optimizing target energy by a global optimum solution. But global optimum may fail to express continuous action or realistically validate how human beings perceive a story. In this paper, we present a bottom-up approach named clip growing for video summarization, which allows users to customize the quality of the video summaries. The proposed approach firstly uses clustering to oversegment video frames into video clips based on their similarity and proximity. Simultaneously, the importance of frames and clips is evaluated from their corresponding dissimilarity and representativeness. Then, video clips and frames are gradually selected according to their energy rank, until reaching the target length. Experimental results on SumMe dataset show that our algorithm can produce promising results compared to existing algorithms. Several video summarizations results are presented in supplementary material.

show abstract

Section: Introductionmentioning

confidence: 99%

A bottom-up summarization algorithm for videos in the wild

Pan

Zheng

Zhang

et al. 2019

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unlike object-based summary event-based summary focus on text and graphics. [40,44,46] 3. Perception Perception based summary considers important content of video which is decided by user perspective.…”

mentioning

confidence: 99%

Recent Challenges and Opportunities in Video Summarization With Machine Learning Algorithms

et al. 2022

View full text Add to dashboard Cite

The fast progress in digital technology has sparked generation of large number of voluminous data from different social media platforms like Instagram, Facebook, YouTube, etc. There are other platforms as well which generate large data like News, CCTV videos, Sports, Entertainment, etc. Lengthy Videos typically contain a significant number of duplicate occurrences that are uninteresting to the viewer. Eliminating this unnecessary information and concentrating only on the crucial events will be far more advantageous. This produces a summary of lengthy films, which can save viewers time and enable better memory management. The highlights of a lengthy video are condensed into a video summary. Video summarization is an essential topic today since many industries have CCTV cameras installed for various reasons such as monitoring, security, and tracking. Because surveillance videos are taken 24 hours a day, enormous amounts of memory and time are required if one wish to trace any incident or person from the full day's video. The Summary generated from multiple view is far more challenging so more study and advancement in MVS is required. The conceptual basis of video summarizing approaches is thoroughly addressed in this paper. This paper addresses applications, technology challenges in Single view and Multi View summarization.

show abstract