Recognizing live fish species by hierarchical partial classification based on the exponential benefit

Live fish recognition in open aquatic habitats suffers from the high uncertainty in many of the data. To alleviate this problem without discarding those data, the system should learn a species hierarchy so that high-level labels can be assigned to ambiguous data. In this paper, a systematic hierarchical partial classification algorithm is therefore proposed for underwater fish species recognition. Partial classification is applied at each level of the species hierarchy so that the coarse-to-fine categorization stops once the decision confidence is low. By defining the exponential benefit function, we formulate the selection of decision threshold as an optimization problem. Also, attributes from important fish anatomical parts are focused to generate discriminative feature descriptors. Experiments show that the proposed method achieves an accuracy up to 94%, with partial decision rate less than 5%, on underwater fish images with high uncertainty and class imbalance.