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- W47514981 abstract "The need to quantify similarity between two groups of objects is prevalent throughout the signal processing world. Traditionally, measures such as the Kullback-Leibler divergence are employed, but these may require expensive computations of covariance or integrals. Maximum mean discrepancy is a modern distance measure that is computationally simpler – involving the inner product between the difference in means of two groups’ feature distributions – yet statistically powerful, because these distributions are mapped into a high-dimensional, nonlinear feature space using kernels, whereupon the means are estimated via the Parzen estimator. We apply this metric and leverage several powerful data representations from the supervised image classification world, such as bag-of-visual-words and sparse combinations of SIFT descriptors, to locate scene change points in videos with promising results." @default.
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- W47514981 date "2013-01-01" @default.
- W47514981 modified "2023-10-06" @default.
- W47514981 title "Unsupervised Visual Changepoint Detection Using Maximum Mean Discrepancy" @default.
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- W47514981 doi "https://doi.org/10.1007/978-3-642-39094-4_38" @default.
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