  Live fish recognition is one of the most crucial elements of fisheries survey
applications where vast amount of data are rapidly acquired. Different from
general scenarios, challenges to underwater image recognition are posted by
poor image quality, uncontrolled objects and environment, as well as difficulty
in acquiring representative samples. Also, most existing feature extraction
techniques are hindered from automation due to involving human supervision.
Toward this end, we propose an underwater fish recognition framework that
consists of a fully unsupervised feature learning technique and an
error-resilient classifier. Object parts are initialized based on saliency and
relaxation labeling to match object parts correctly. A non-rigid part model is
then learned based on fitness, separation and discrimination criteria. For the
classifier, an unsupervised clustering approach generates a binary class
hierarchy, where each node is a classifier. To exploit information from
ambiguous images, the notion of partial classification is introduced to assign
coarse labels by optimizing the "benefit" of indecision made by the classifier.
Experiments show that the proposed framework achieves high accuracy on both
public and self-collected underwater fish images with high uncertainty and
class imbalance.
