Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11950
Title: User-centred personalised video abstraction approach adopting SIFT features
Authors: Darabi, K
Ghinea, G
Keywords: Video summarization;SIFT;Personalization;Saliency score;Relevancy level
Issue Date: 2016
Publisher: Springer Verlag
Citation: Multimedia Tools and Applications, pp. 1 - 26, (2016)
Abstract: The rapid growth of digital video content in recent years has imposed the need for the development of technologies with the capability to produce condensed but semantically rich versions of original input video. Consequently, the topic of Video Summarisation is becoming increasingly popular in the multimedia community and numerous video abstraction approaches have been proposed. Creating personalised video summaries remains a challenge, though. Accordingly, in this paper we propose a methodology for generating user-tailored video abstracts. First, video frames are scored by a group of video experts (operators) according to audio, visual and textual content of the video. Later, SIFT visual features are adopted in our proposed approach to identify the video scenes’ semantic categories. Fusing this retrieved data with pre-built users’ profiles will provide a metric to update the previously averaged saliency scores assigned by video experts to each frame in accordance to users’ priorities. In the next stage, the initial averaged scores of the frames are updated based on the end-users’ generated profiles. Eventually, the highest scored video frames alongside the auditory and textual content are inserted into final digest Experimental results showed the effectiveness of this method in delivering superior outcomes comparing to our previously recommended algorithm and the three other automatic summarisation techniques.
URI: http://link.springer.com/article/10.1007%2Fs11042-015-3210-4
http://bura.brunel.ac.uk/handle/2438/11950
DOI: http://dx.doi.org/10.1007/s11042-015-3210-4
ISSN: 1380-7501
1573-7721
Appears in Collections:Dept of Computer Science Research Papers

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