Raw visual data used to train classifiers is abundant and easy to gather, but lacks semantic labels that describe visual concepts of interest. These labels are necessary for supervised learning and can require significant human effort to collect. We discuss four labeling objectives that play an important role in the design of frameworks aimed at collecting label information for large training sets while maintaining low human effort: discovery, efficiency, exploitation and accuracy. We introduce a framework that explicitly models and balances these four labeling objectives with the use of (1) hierarchical clustering, (2) a novel interestingness measure that defines structural change within the hierarchy, and (3) an iterative group-based labeling process that exploits relationships between labeled and unlabeled data. Results on benchmark data show that our framework collects labeled training data more efficiently than existing labeling techniques and trains higher performing visual classifiers. Further, we show that our resulting framework is fast and significantly reduces human interaction time when labeling real-world multi-concept imagery depicting outdoor environments.

中文翻译：

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通过分层聚类对图像数据集进行高效的标签收集

用于训练分类器的原始视觉数据丰富且易于收集，但缺乏描述感兴趣的视觉概念的语义标签。这些标签对于监督学习是必要的，并且可能需要大量的人力来收集。我们讨论了四个标签目标，它们在旨在为大型训练集收集标签信息同时保持低人力的框架设计中发挥重要作用：发现、效率、利用和准确性。我们引入了一个框架，该框架使用（1）层次聚类，（2）一种定义层次结构内结构变化的新颖有趣度度量，以及（3）一个基于迭代的基于组的标记过程，显式地对这四个标记目标进行建模和平衡利用标记和未标记数据之间的关系。基准数据的结果表明，我们的框架比现有标记技术更有效地收集标记训练数据，并训练更高性能的视觉分类器。此外，我们表明，在标记描绘室外环境的真实世界多概念图像时，我们得到的框架速度很快，并显着减少了人机交互时间。