  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.
