论文标题
一种迭代标签方法，用于注释渔业图像
An Iterative Labeling Method for Annotating Fisheries Imagery
论文作者
论文摘要
在本文中，我们提出了一种与渔业相关数据的方法，该方法使我们能够通过多个可以利用众包接口的培训和生产循环在数据集上迭代标记的图像数据集。我们将算法及其结果介绍在使用海底自动水下车辆收集的两组单独的图像数据上。第一个数据集由2,026个完全未标记的图像组成，而第二个数据集由21,968张图像组成，这些图像由专家注释。我们的结果表明，使用小子集的培训并迭代以构建较大的标记数据，从而使我们能够收敛到带有少量迭代的完全注释的数据集。即使在专家标记的数据集的情况下，该方法论的单个迭代也通过发现与与水下图像相关的对比度限制的其他标签相关的其他标签的复杂示例来改善标签。
In this paper, we present a methodology for fisheries-related data that allows us to converge on a labeled image dataset by iterating over the dataset with multiple training and production loops that can exploit crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset comprises of 2,026 completely unlabeled images, while the second consists of 21,968 images that were point annotated by experts. Our results indicate that training with a small subset and iterating on that to build a larger set of labeled data allows us to converge to a fully annotated dataset with a small number of iterations. Even in the case of a dataset labeled by experts, a single iteration of the methodology improves the labels by discovering additional complicated examples of labels associated with fish that overlap, are very small, or obscured by the contrast limitations associated with underwater imagery.
