A comparative study applied to dynamic textures segmentation

摘要

As textures in motion, dynamic textures (DT) are video sequences that are spatially and temporally repetitive. Since, the segmentation of this type of textures represents many difficulties; this paper proposes a comparative study between neural, fuzzy and statistical methods for DT segmentation. In order to bring the best results for DT classification, the proposed method is based on an extraction of two major areas of sequence into two categories: static zone (SZ) and dynamic zone (DZ). Then, for the same data, we applied our methods to compare between dynamic textures DT and static textures (ST). By extracting the best method and thanks to the Multilayer artificial Neural Network (MNN) segmentation, 82% is achieved as classification accuracy. Finally, the experimental results show that the three used scenes are successfully applied to DT.