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Group-based spatio-temporal video analysis and abstraction using wavelet parameters

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Abstract

In this paper, we present a spatio-temporal event-based approach to video signal analysis and abstraction employing wavelet transform features. The video signal is assumed to be a sequence of overlapping independent visual components called events, which typically are temporally overlapping compact functions that describe temporal evolution of a given set of the spatial parameters of the video signal. We utilize event-based temporal decomposition technique to resolve the overlapping arrangement of the video signal that is known to be one of the main concerns in video analysis via conventional frame-based schemes. In our method, a set of spatial parameters, extracted from the video, is expressed as a linear combination of a set of temporally overlapping compact functions, called events, through an optimization process. First, to reduce computational complexity, the video sequence is divided into overlapped groups. Next, Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform subbands, are used as the spatial parameters. Temporal decomposition is then applied to the GGD parameters, structured as a frame-based matrix of GGD vectors, to compute the event functions and associated orthogonal GGD parameters. Frames located at event centroids, which are much smaller in number than the number of frames in the original video, are taken as candidates for the keyframes that are selected based on a distance criterion in the feature space. Our contribution is that this still image video abstraction scheme does not need shot or cluster boundary detection, unlike current methods. Experimental results confirm the efficiency and accuracy of our approach.

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Authors and Affiliations

  1. Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

    M. Omidyeganeh & S. Ghaemmaghami

  2. Electronics Research Institute, Sharif University of Technology, Azadi Ave., P.O. Box 11155-8639, Tehran, Iran

    S. Ghaemmaghami

  3. DISCOVER Laboratory, School of Information Technology and Engineering, University of Ottawa, 800 King Edward Ave., Ottawa, ON, K1N 6N5, Canada

    M. Omidyeganeh & S. Shirmohammadi

Authors
  1. M. Omidyeganeh
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  2. S. Ghaemmaghami
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  3. S. Shirmohammadi
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Correspondence to M. Omidyeganeh.

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Omidyeganeh, M., Ghaemmaghami, S. & Shirmohammadi, S. Group-based spatio-temporal video analysis and abstraction using wavelet parameters. SIViP 7, 787–798 (2013). https://doi.org/10.1007/s11760-011-0268-y

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