Categorization of Underwater Habitats Using Dynamic Video Textures
In this paper, we deal with the problem of categorizing different underwater habitat types. Previous works on solving this categorization problem are mostly based on the analysis of underwater images. In our work, we design a system capable of categorizing underwater habitats based on underwater video content analysis since the temporally correlated information may make contribution to the categorization task. However, the task is very challenging since the underwater scene in the video is continuously varying because of the changing scene and surface conditions, lighting, and the viewpoint. To that end, we investigate the utility of two approaches to underwater video classification: the common spatio-temporal interest points (STIPs) and the video texture dynamic systems, where we model the underwater footage using dynamic textures and construct a categorization framework using the approach of the Bag-of-Systems(BoSs). We also introduce a new underwater video data set, which is composed of more than 100 hours of annotated video sequences. Our results indicate that, for the underwater habitat identification, the dynamic texture approach has multiple benefits over the traditional STIP-based video modeling.