Gaussian mixture vector quantization-based video summarization using independent component analysis

In this paper, we propose a new Gaussian mixture vector quantization (GMVQ)-based method to summarize the video content. In particular, in order to explore the semantic characteristics of video data, we present a new feature extraction method using independent component analysis (ICA) and color histogram difference to build a compact 3D feature space first. A new GMVQ method is then developed to find the optimized quantization codebook. The optimal codebook size is determined by Bayes information criterion (BIC). The video frames that are the nearest-neighbours to the quanta in the GMVQ quantization codebook are sampled to summarize the video content. A kD-tree-based nearest-neighbour search strategy is employed to accelerate the search procedure. Experimental results show that our method is computationally efficient and practically effective to build a content-based video summarization system.