16 research outputs found
VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering
In this paper, we present VSCAN, a novel approach for generating static video
summaries. This approach is based on a modified DBSCAN clustering algorithm to
summarize the video content utilizing both color and texture features of the
video frames. The paper also introduces an enhanced evaluation method that
depends on color and texture features. Video Summaries generated by VSCAN are
compared with summaries generated by other approaches found in the literature
and those created by users. Experimental results indicate that the video
summaries generated by VSCAN have a higher quality than those generated by
other approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1401.3590 by
other authors without attributio
Towards key-frame extraction methods for 3D video: a review
The increasing rate of creation and use of 3D video content leads to a pressing need for methods capable of lowering
the cost of 3D video searching, browsing and indexing operations, with improved content selection performance.
Video summarisation methods specifically tailored for 3D video content fulfil these requirements. This paper presents
a review of the state-of-the-art of a crucial component of 3D video summarisation algorithms: the key-frame
extraction methods. The methods reviewed cover 3D video key-frame extraction as well as shot boundary detection
methods specific for use in 3D video. The performance metrics used to evaluate the key-frame extraction methods
and the summaries derived from those key-frames are presented and discussed. The applications of these methods
are also presented and discussed, followed by an exposition about current research challenges on 3D video
summarisation methods