TY - JOUR

T1 - Classification and Comparison of On-Line Video Summarisation Methods

AU - Matthews, Clare E.

AU - Kuncheva, Ludmila I.

AU - Yousefi, Paria

PY - 2019/4

Y1 - 2019/4

N2 - Many methods exist for generating keyframe summaries of videos. However, relatively few methods consider on-line summarisation, where memory constraints mean it is not practical to wait for the full video to be available for processing. We propose a classification (taxonomy) for on-line video summarisation methods based upon their descriptive and distinguishing properties such as feature space for frame representation, strategies for grouping time-contiguous frames, and techniques for selecting representative frames. Nine existing on-line methods are presented within the terms of our taxonomy, and subsequently compared by testing on two synthetic data sets and a collection of short videos. We find that success of the methods is largely independent of techniques for grouping time-contiguous frames and for measuring similarity between frames. On the other hand, decisions about the number of keyframes and the selection mechanism may substantially affect the quality of the summary. Finally we remark on the difficulty in tuning the parameters of the methods ``on-the-fly'', without knowledge of the video duration, dynamic or content.

AB - Many methods exist for generating keyframe summaries of videos. However, relatively few methods consider on-line summarisation, where memory constraints mean it is not practical to wait for the full video to be available for processing. We propose a classification (taxonomy) for on-line video summarisation methods based upon their descriptive and distinguishing properties such as feature space for frame representation, strategies for grouping time-contiguous frames, and techniques for selecting representative frames. Nine existing on-line methods are presented within the terms of our taxonomy, and subsequently compared by testing on two synthetic data sets and a collection of short videos. We find that success of the methods is largely independent of techniques for grouping time-contiguous frames and for measuring similarity between frames. On the other hand, decisions about the number of keyframes and the selection mechanism may substantially affect the quality of the summary. Finally we remark on the difficulty in tuning the parameters of the methods ``on-the-fly'', without knowledge of the video duration, dynamic or content.

U2 - 10.1007/s00138-019-01007-x

DO - 10.1007/s00138-019-01007-x

M3 - Article

VL - 30

SP - 507

EP - 518

JO - Machine Vision and Applications

JF - Machine Vision and Applications

SN - 0932-8092

IS - 3

ER -