Document of bibliographic reference 325221

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

MorphoCluster: Efficient annotation of plankton images by clustering

Abstract

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 1.2 M objects into 280 data-driven classes in 71 h (16 k 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

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000552737900053

Bibliographic citation

Schröder, S.-M.; Kiko, R.; Koch, R. (2020). MorphoCluster: Efficient annotation of plankton images by clustering. Sensors 20(11): 3060. https://dx.doi.org/10.3390/s20113060

Is peer reviewed

true

Access rights

open access

Is accessible for free

true