Spatial image polynomial decomposition with application to video classification

Redouane El Moubtahij; Bertrand Augereau; Hamid Tairi; Christine Fernandez-Maloigne

doi:10.1117/1.JEI.24.6.061114

12 November 2015 Spatial image polynomial decomposition with application to video classification

Redouane El Moubtahij, Bertrand Augereau, Hamid Tairi, Christine Fernandez-Maloigne

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 6, 061114 (November 2015). https://doi.org/10.1117/1.JEI.24.6.061114

Abstract

This paper addresses the use of orthogonal polynomial basis transform in video classification due to its multiple advantages, especially for multiscale and multiresolution analysis similar to the wavelet transform. In our approach, we benefit from these advantages to reduce the resolution of the video by using a multiscale/multiresolution decomposition to define a new algorithm that decomposes a color image into geometry and texture component by projecting the image on a bivariate polynomial basis and considering the geometry component as the partial reconstruction and the texture component as the remaining part, and finally to model the features (like motion and texture) extracted from reduced image sequences by projecting them into a bivariate polynomial basis in order to construct a hybrid polynomial motion texture video descriptor. To evaluate our approach, we consider two visual recognition tasks, namely the classification of dynamic textures and recognition of human actions. The experimental section shows that the proposed approach achieves a perfect recognition rate in the Weizmann database and highest accuracy in the Dyntex++ database compared to existing methods.

Citation Download Citation

Redouane El Moubtahij, Bertrand Augereau, Hamid Tairi, and Christine Fernandez-Maloigne "Spatial image polynomial decomposition with application to video classification," Journal of Electronic Imaging 24(6), 061114 (12 November 2015). https://doi.org/10.1117/1.JEI.24.6.061114

Published: 12 November 2015

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