Evolutionary Computational Methods for Optimizing the Classification of Sea Stars in Underwater Images

Using video and imagery for assessing the distribution and abundance of marine organisms is a valuable sampling method in that it is non-invasive and permits large volumes of data to be acquired. Quickly and accurately processing large volumes of imagery is a challenge for human analysts, which motivates the need for automated processing methods. In this paper, we present a method for the automatic classification of sea stars in underwater images. The method uses a very small number of features and is efficient. The classification process is optimized by using evolutionary computational methods. Experimental results show excellent performance of our proposed optimized classification approach.