Open AccessVector ordering and regression learning‐based ranking for dynamic summarisation of user videos
Author(s) -
K Vivekraj V,
Sen Debashis,
Raman Balasubramanian
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0234
Subject(s) - ranking (information retrieval) , computer science , frame (networking) , artificial intelligence , process (computing) , selection (genetic algorithm) , ranking svm , feature (linguistics) , machine learning , pattern recognition (psychology) , telecommunications , operating system , linguistics , philosophy
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.