Effective key‐frame extraction approach using TSTBTC–BBA

Singh, Yadwinder; Kaur, Lakhwinder

doi:10.1049/iet-ipr.2018.6361

Cited by 4 publications

(2 citation statements)

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“…Zhou et al [39] approach the problem using reinforcement learning for creating diverse and representative summaries, while Zhang et al [42] identified key objects and their motions for detecting important parts of the video. Block sparse coding-based reconstruction is experimented in [46][47][48]. Summarisation of multi-view scenes is performed in [35,36] using semantic information.…”

Section: Unsupervised Skimming Techniques For User Videosmentioning

confidence: 99%

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Vector ordering and regression learning‐based ranking for dynamic summarisation of user videos

Sen

Raman

2020

IET Image Processing

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Dynamic video summarisation (video skimming) is a process of generating a shorter video (video skim) as a summary of a given video, which helps in its easier and quicker comprehension. In this study, an efficient dynamic summarisation approach for user videos is proposed using vector ordering for ranking video units (frames/shots). User videos are casually shot unscripted videos, where skimming involves the selection of its interesting part(s) ignoring many uninteresting ones. The concept of R-ordering of vectors is employed to find a representative frame, which is used to perform relative ranking of the video frames. It is theoretically shown that significance is given to each element of a frame's feature vector while computing the importance scores that lead to the frame ranks used for skimming. Furthermore, the allocation of different weights to the features involved is also achieved using linear and Gaussian process regressions. Through extensive experiments considering several standard datasets with human-labelled ground truth, the proposed approach is demonstrated to be efficient and to perform better than the relevant state-of-the-art.

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Section: Unsupervised Skimming Techniques For User Videosmentioning

confidence: 99%

“…[42] identified key objects and their motions for detecting important parts of the video. Block sparse coding‐based reconstruction is experimented in [46–48]. Summarisation of multi‐view scenes is performed in [35, 36] using semantic information.…”

Section: Related Workmentioning

confidence: 99%