Boosting rare benthic macroinvertebrates taxa identification with one-class classification
This addresses the challenge of scaling up insect monitoring for ecological research by partially automating taxa identification, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of identifying rare benthic macroinvertebrates taxa in insect monitoring, where deep CNNs struggle due to imbalanced data and limited training samples for rare classes, and proposed combining deep CNNs with one-class classifiers to improve rare species identification, with experiments confirming the approach supports partial automation of the task.
Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be scaled-up efficiently. Deep convolutional neural networks (CNNs), provide a viable way to significantly increase the biomonitoring volumes. However, taxa abundances are typically very imbalanced and the amounts of training images for the rarest classes are simply too low for deep CNNs. As a result, the samples from the rare classes are often completely missed, while detecting them has biological importance. In this paper, we propose combining the trained deep CNN with one-class classifiers to improve the rare species identification. One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection. Our experiments confirm that the proposed approach may indeed support moving towards partial automation of the taxa identification task.