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We propose a new algorithm for sequence segmentation based on recent advances in semi-parametric sequence clustering. This approach implies the use of model-based distance measures between sequences, as well as a variant of spectral clustering specially tailored for segmentation. The method is highly flexible since it allows for the use of any probabilistic generative model for the individual segments...
The accuracy of the Cauchy probability density function for modeling of the discrete cosine transform coefficient distribution has already been proved for the frame layer of the rate control subsystem of a hybrid video coder. Nevertheless, in some specific applications operating in real-time low-delay environments, a basic unit layer is recommended in order to provide a good trade-off between quality...
In this paper, we propose a new quantifier fuzzification mechanism which is deeply rooted in the theory of probability. This quantifier fuzzification mechanism skips the nested assumption, which is inherent to other probabilistic quantification methods. The new quantification approach complies with the properties required for determiner fuzzification schemes (DFS) with finite sets and, hence, its...
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