Spatial image polynomial decomposition with application to video classification

Redouane El Moubtahij (1, 2) , Bertrand Augereau (1) , Hamid Tairi (2) , Christine Fernandez-Maloigne (1)

1 XLIM-SIC - SIC
2 LIIAN - Laboratoire d'Informatique, Imagerie et Analyse Numérique

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.

Mots clés: video classification; texture extraction; polynomial decomposition; dynamic texture

Domaines: Traitement des images [eess.IV]; Imagerie médicale

Redouane El Moubtahij, Bertrand Augereau, Hamid Tairi, Christine Fernandez-Maloigne. Spatial image polynomial decomposition with application to video classification. Journal of Electronic Imaging, 2015, Special Section on Quality Control by Artificial Vision: Nonconventional Imaging Systems, 24 (Issue 6), ⟨10.1117/1.JEI.24.6.061114⟩. ⟨hal-01228892⟩