Unified dynamic texture segmentation system based on local and global spatiotemporal techniques

Abstract

Dynamic texture (DT) is temporal extension of static texture. There are two broad types of dynamic textures: natural and manmade. The examples of dynamic textures are waving tree, sea water, fountain, traffic, moving crowd, etc. Dynamic texture segmentation is a technique used to separate the moving objects from stationary content in the video. Majority of the techniques give good results either for natural or for manmade dynamic textures. The proposed approach for DT Segmentation is combination of local spatiotemporal technique, i.e., local binary pattern-Weber local descriptor and global spatiotemporal technique, i.e., contourlet transform. The local spatiotemporal technique considers the appearance and motion of the object for segmentation. The technique is computationally less complex than optical flow and gives good results for manmade dynamic textures. The global spatiotemporal technique is based on Laplacian pyramid and directional filters. It gives good results for natural dynamic textures. The proposed technique discussed in this paper is unified approach for any type of dynamic texture. The regions of images commonly segmented by both the techniques are considered as the final segmented output. The proposed system works equally well on any kind of dynamic texture.