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 tex- ture component by projecting the image on a bivariate polynomial basis and considering the geometry compo- nent 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 recog- nition of human actions. The experimental section shows that the proposed approach achieves a perfect rec- ognition rate in the Weizmann database and highest accuracy in the Dyntex++ database compared to existing methods. © 2015 SPIE and IS&T (DOI: 10.1117/1.JEI.24.6.061114)
['Redouane El Moubtahij', 'Bertrand Augereau', 'Hamid Tairi', 'Christine Fernandez-Maloigne']
Spatial image polynomial decomposition with application to video classification