Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke





.......................

.......................


Publications [#265121] of Guillermo Sapiro

Papers Published

  1. Castrodad, A; Sapiro, G, Sparse modeling of human actions from motion imagery, International Journal of Computer Vision, vol. 100 no. 1 (2012), pp. 1-15, ISSN 0920-5691 [doi]
    (last updated on 2017/12/15)

    Abstract:
    An efficient sparse modeling pipeline for the classification of human actions from video is here developed. Spatio-temporal features that characterize local changes in the image are first extracted. This is followed by the learning of a class-structured dictionary encoding the individual actions of interest. Classification is then based on reconstruction, where the label assigned to each video comes from the optimal sparse linear combination of the learned basis vectors (action primitives) representing the actions. A low computational cost deep-layer model learning the inter-class correlations of the data is added for increasing discriminative power. In spite of its simplicity and low computational cost, the method outperforms previously reported results for virtually all standard datasets. © 2012 Springer Science+Business Media, LLC (outside the USA).

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320