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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Earth Observation Image Semantic Bias: A Collaborative User Annotation Approach
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Earth Observation Image Semantic Bias: A Collaborative User Annotation Approach

机译：地球观测图像语义偏向：一种协作的用户注释方法

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摘要

Correctly annotated image datasets are important for developing and validating image mining methods. However, there is some doubt regarding the generalizability of the models trained and validated on available datasets. This is due to dataset biases, which occur when the same semantic label is used in different ways across datasets, and/or when identical object categories are labeled differently across datasets. In this paper, we demonstrate the existence of dataset biases with a sample of eight remote sensing image datasets, first showing they are readily discriminable from a feature perspective, and then demonstrating that a model trained on one dataset is not always valid on others. Past approaches to reducing dataset biases have relied on crowdsourcing, however this is not always an option (e.g., due to public-accessibility restrictions of images), raising the question: How to structure annotation tasks to efficiently and accurately annotate images with a limited number of nonexpert annotators? We propose a collaborative annotation methodology, conducting image annotation experiments where users are placed in either a collaborative or individual condition, and we analyze their annotation performance. Results show the collaborators produce more thorough, precise annotations, requiring less time than the individuals. Collaborators labels show less variance around the consensus point, meaning their assigned labels are more predictable and likely to be generally accepted by other users. Therefore, collaborative image annotation is a promising annotation methodology for creating reliable datasets with a reduced number of nonexpert annotators. This in turn has implications for the creation of less biased image datasets.
机译：正确注释的图像数据集对于开发和验证图像挖掘方法很重要。但是，对于在可用数据集上训练和验证的模型的一般性存在一些疑问。这是由于数据集偏差造成的，当在整个数据集中以相同的方式使用相同的语义标签时，和/或在相同的对象类别在数据集中以不同的方式进行标记时，就会发生偏差。在本文中，我们通过八个遥感图像数据集的样本来证明数据集偏差的存在，首先显示从特征角度可以容易地区分它们，然后证明在一个数据集上训练的模型并不总是对其他数据集有效。过去减少数据集偏差的方法一直依赖于众包，但是这并不总是一种选择（例如，由于图像的公共可访问性限制），这引发了一个问题：如何构造注释任务以有效且准确地注释数量有限的图像非专家注释者？我们提出了一种协作注释方法，进行将用户置于协作或个人条件下的图像注释实验，并分析其注释性能。结果表明，协作者可以产生更彻底，更精确的注释，所需的时间少于个人。协作者标签在共识点附近的差异较小，这意味着分配给他们的标签更具可预测性，并且很可能被其他用户普遍接受。因此，协作图像注释是一种有前途的注释方法，可用于创建可靠的数据集并减少非专家注释符的数量。反过来，这对创建偏差较小的图像数据集也有影响。

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