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Gauss mixture vector quantization
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3rd international workshop on automated information extraction in media production
Proceedings of the international conference on Multimedia
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In this paper, we propose a new spatiotemporal vector quantization method to create video thumbnail. In particular, we present a novel video data modeling tools, video time density function (VTDF) to explore the temporal characteristics of video content. A VTDF-based temporal quantization is applied to segment video data in time domain. The optimal number of segments is obtained by a temporal mean square error (TMSE)-based criterion. For each segment, we use independent component analysis (ICA) to build a compact 2D feature space first. A Gaussian mixture-based vector quantization method is then employed to explore the spatial characteristics of each segment. The optimal number of Gaussian components is determined by Bayes information criterion (BIC). The video frames that are the nearest neighbors to the quantization codebook are extracted to abstract the whole segment. Experimental results show that our method is computationally efficient and practically effective to create content-based video thumbnail.