Supervised and Unsupervised Feature Extraction Methods for Underwater Fish Species Recognition

Automated fish species identification in open aquatic habitats based on video analytics is the primary area of research in camera-based fisheries surveys. Finding informative features for these analyses, however, is fundamentally challenging due to poor quality of underwater imagery and strong visual similarity among species. In this paper, we compare two different fish feature extraction methods, namely the supervised and unsupervised approaches, which are then applied to a hierarchical partial classification framework. Several specified anatomical parts of fish are automatically located to generate the supervised feature descriptors. For unsupervised feature extraction, a scale-invariant object part learning algorithm is proposed to discover common shape of body parts and then extract appearance, location and size information of each part. Experiments show that the unsupervised approach achieves better recognition performance on live fish images collected by trawl-based cameras.