Fish Species Classification Using Graph Embedding Discriminant Analysis

Fish Species classification is of great utility to marine biologists for the understanding of underwater ecology and fish behavior as well as to keep a log of endangered species, which assists in fisheries management. Traditional methods being either tedious or too computationally intensive lead to the requirement of an automated method of analysis and counting. Generally, the classification of underwater images is challenging due to difficulties in camera calibrations, which lead to distortion, background noise, occlusion and image quality to name a few. This paper proposes a novel method based on an improved image-set matching approach, which makes use of Graph-Embedding Discriminant Analysis. In contrast to the state-of-the-art methods, which operate on single input images, our method makes, use of explicit image set matching which renders it robust computationally. In comparison with the previous classification methods, this method results in considerable discrimination accuracy improvements.