Dynamic Texture Segmentation Approaches for Natural and Manmade Cases: Survey and Experimentation

Abstract

Dynamic Textures are temporal extension of static textures. Texture is defined as an image with some repetitive pattern in it. Dynamic texture (DT) is nothing but video data with some stationary properties and some moving objects in it. DT segmentation is a broad branch of DT Analysis. Segmentation is separating disjoint regions in the image frames which have homogeneous properties such as texture, color, motion. An important role is played by it in the applications like forest fire detection, traffic density detection, crowd congestion detection before stampede, auto pilot airplanes. There are various approaches used for DT segmentation. In this paper, the approaches based on optical flow, local spatiotemporal technique (Local Binary Pattern) and Global spatiotemporal technique (Contourlet transform) are discussed. Each technique is used and modified or combined with some other technique by researchers. This paper gives an overview of all techniques along with variations in them and the benefits achieved by using them on DT dataset. Some experimental results are also presented.