MorphoCluster: Efficient Annotation of Plankton Images by Clustering
MorphoCluster: Efficient Annotation of Plankton Images by Clustering
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DOI：
10.3390/s20113060
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Simon-Martin Schröder;R. Kiko;R. Koch
中科院分区：
文献类型：
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作者：
Simon-Martin Schröder;R. Kiko;R. Koch
在这项工作中，我们提出了MorphoCluster，一个软件工具，用于数据驱动，快速，准确的大型图像数据集的注释。虽然海洋数据的注释速度已经超过人类专家的注释速度，但在未来几年中，海洋数据的数量和复杂性将继续增加。然而，这些数据需要解释。MorphoCluster通过在交互过程中嵌入无监督聚类，增强了人类在大量数据中发现模式和执行对象分类的能力。通过将相似的图像聚合到集群中，我们的图像注释新方法提高了一致性，增加了注释器的吞吐量，并允许专家根据数据结构调整其排序方案的粒度。通过在71小时内将一组120万个对象分类为280个数据驱动类（每小时16 k个对象），其中90%的类具有0.889或更高的精度。这表明MorphoCluster同时快速，准确和一致;提供细粒度和数据驱动的分类;并实现新奇检测。
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.