Computer vision algorithms are increasingly being applied to museum collections to identify patterns, colors, and subjects by generating tags for each object image. There are multiple off-the-shelf systems that offer an accessible and rapid way to undertake this process. Based on the highlights of the Metropolitan Museum of Art's collection, this article examines the similarities and differences between the tags generated by three well-known computer vision systems (Google Cloud Vision, Amazon Rekognition, and IBM Watson). The results provide insights into the characteristics of these taxonomies in terms of the volume of tags generated for each object, their diversity, typology, and accuracy. In consequence, this article discusses the need for museums to define their own subject tagging strategy and selection criteria of computer vision tools based on their type of collection and tags needed to complement their metadata.

中文翻译：

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计算机视觉标记大都会艺术博物馆的藏品

计算机视觉算法越来越多地应用于博物馆藏品，通过为每个对象图像生成标签来识别图案、颜色和主题。有多个现成的系统提供了一种可访问且快速的方式来执行此过程。本文基于大都会艺术博物馆的收藏亮点，考察了三种知名计算机视觉系统（Google Cloud Vision、Amazon Rekognition 和 IBM Watson）生成的标签之间的异同。结果提供了对这些分类法特征的洞察，包括为每个对象生成的标签数量、它们的多样性、类型和准确性。结果，