A Whale's Tail - Finding the Right Whale in an Uncertain World

Explainable machine learning and uncertainty quantification have emerged as promising approaches to check the suitability and understand the decision process of a data-driven model, to learn new insights from data, but also to get more information about the quality of a specific observation. In particular, heatmapping techniques that indicate the sensitivity of image regions are routinely used in image analysis and interpretation. In this paper, we consider a landmark-based approach to generate heatmaps that help derive sensitivity and uncertainty information for an application in marine science to support the monitoring of whales. Single whale identification is important to monitor the migration of whales, to avoid double counting of individuals and to reach more accurate population estimates. Here, we specifically explore the use of fluke landmarks learned as attention maps for local feature extraction and without other supervision than the whale IDs. These individual fluke landmarks are then used jointly to predict the whale ID. With this model, we use several techniques to estimate the sensitivity and uncertainty as a function of the consensus level and stability of localisation among the landmarks. For our experiments, we use images of humpback whale flukes provided by the Kaggle Challenge Humpback Whale Identification and compare our results to those of a whale expert.

Automated summary

Explainable machine learning and uncertainty quantification have emerged as promising approaches in understanding decision processes. In this paper, a landmark-based approach using heatmapping techniques is proposed to derive sensitivity and uncertainty information for monitoring whales. Experimental results show that this method is more accurate compared to traditional methods.