Underwater Image Segmentation with the GMM (Gaussian Mixture Model) Algorithm

Abstract

The purpose of this study was to measure the performance of Gaussian Mixture Model (GMM) technique for underwater image segmentation of seagrass objects based on datasets from autonomous surface vehicles (ASV from the Faculty of Fisheries and Marine Sciences, Bogor Agricultural University. The dataset is 640 x 480 pixel image data to support image segmentation research. There are three categories of underwater imagery: (a) underwater imagery featuring seagrass and seawater backgrounds; (b) underwater imagery featuring seagrasses, clear fish, and seawater backgrounds; and (c) underwater imagery featuring seagrasses, faint fish, and seawater backgrounds. Based on the experimental results, seagrass objects in image type (a) have almost identical colors to each pixel in the underwater image, the GMM model was able to distinguish them from the background and seawater background. The GMM model can distinguish between the background and the seawater background in image type (b), but cannot eliminate fish objects in the image. The segmentation results in image type (c) are not perfect because the GMM model removes seagrass objects that have green pixel color.