Browsing by Subject "High-dimensional space"

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    (IEEE, 2013-04) Adıgüzel, Hande; Erdem, Hayrettin; Ferhatosmanoǧlu, Hakan; Duygulu, Pınar
    Analyzing and interpreting human actions is an important and challenging area of computer vision. Different solutions are used for representing human actions; we prefer to use spatio-temporal interest points for motion descriptors. Besides, the space-time interest point feature space is considerably high-dimensional and it is hard to eliminate the curse of dimensionality with traditional similarity functions. We apply a matching based approach for high dimensional feature space that matches sequences to classify actions. © 2013 IEEE.