MorphoCluster: Efficient Annotation of Plankton images by Clustering
This tool addresses the need for fast and accurate annotation of marine plankton images, which is crucial for experts dealing with increasing data volumes, though it is an incremental improvement in annotation methods.
The authors tackled the problem of efficiently annotating large plankton image datasets by developing MorphoCluster, a software tool that uses interactive clustering to achieve an annotation rate of 16k objects per hour with high precision.
In this work, we present MorphoCluster, a software tool for data-driven, fast and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2M objects into 280 data-driven classes in 71 hours (16k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate and consistent, provides a fine-grained and data-driven classification and enables novelty detection. MorphoCluster is available as open-source software at https://github.com/morphocluster.