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Live fish recognition is one of the most crucial elements of fisheries surveyapplications where vast amount of data are rapidly acquired. Different fromgeneral scenarios, challenges to underwater image recognition are posted bypoor image quality, uncontrolled objects and environment, as well as difficultyin acquiring representative samples. Also, most existing feature extractiontechniques are hindered from automation due to involving human supervision.Toward this end, we propose an underwater fish recognition framework thatconsists of a fully unsupervised feature learning technique and anerror-resilient classifier. Object parts are initialized based on saliency andrelaxation labeling to match object parts correctly. A non-rigid part model isthen learned based on fitness, separation and discrimination criteria. For theclassifier, an unsupervised clustering approach generates a binary classhierarchy, where each node is a classifier. To exploit information fromambiguous images, the notion of partial classification is introduced to assigncoarse labels by optimizing the "benefit" of indecision made by the classifier.Experiments show that the proposed framework achieves high accuracy on bothpublic and self-collected underwater fish images with high uncertainty andclass imbalance.

A Feature Learning and Object Recognition Framework for Underwater Fish Images