Feature extraction and clustering for dynamic video summarisation

Authors:
Huiyu Zhou;Abdul H. Sadka;Mohammad R. Swash;Jawid Azizi;Umar A. Sadiq
Affiliations:
School of Engineering and Design, Brunel University, UB8 3PH, UK;School of Engineering and Design, Brunel University, UB8 3PH, UK;School of Engineering and Design, Brunel University, UB8 3PH, UK;School of Engineering and Design, Brunel University, UB8 3PH, UK;School of Engineering and Design, Brunel University, UB8 3PH, UK
Venue:
Neurocomputing
Year:
2010

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Abstract

In this paper an effective dynamic video summarisation algorithm is presented using audio-visual features extracted from videos. Audio, colour and motion features are dynamically fused using an adaptively weighting mechanism. Dissimilarities of temporal video segments are formulated using the extracted features before these segments are clustered using a fuzzy c-means algorithm with an optimally determined cluster number. The experimental results demonstrate the ability of the proposed algorithm to automatically summarise the videos with good performance.