Automatic searching of fish from underwater images via shape matching

To conserve and manage fish stocks in fisheries, people monitor the growth and quantities of fish by collected underwater videos and images. However, the efficiency of finding fish from videos and images is limited by manual operation. In this paper, we propose an automatic method to improve the efficiency of finding fish by shape matching. In our method, a segmentation method, Segmentation by Aggregating Superpixels (SAS), is used to partition all objects from every underwater image. To search for fish from these partitioned objects, Inner-Distance Shape Context (IDSC) is applied as a shape matching method. The proposed method can overcome the disturbing of specific underwater environment, such as blurring and fish's deformation in images. Experimental result shows that the method is effective to search for fish from underwater images and it can be widely applied to fish surveillance in fisheries.