Vector ordering and regression learning‐based ranking for dynamic summarisation of user videos
| Autor: | Debashis Sen, Vivekraj V K, Balasubramanian Raman |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Shot (filmmaking) Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Machine learning computer.software_genre Ranking (information retrieval) symbols.namesake 0202 electrical engineering electronic engineering information engineering Selection (linguistics) Electrical and Electronic Engineering Gaussian process Ground truth business.industry Frame (networking) Process (computing) 020206 networking & telecommunications Signal Processing symbols 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Software |
| Zdroj: | IET Image Processing. 14:3941-3956 |
| ISSN: | 1751-9667 1751-9659 |
| DOI: | 10.1049/iet-ipr.2020.0234 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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