After two decades of steady increases in image resolution through technical advances in image sensors, we are now also witnessing a significant growth in comprehensively tagged image collections. Concurrently cultural institutions have been digitizing their collections, while cultural scholars have been investing considerable efforts into the annotation of images to denote iconographic details and historical context. Despite these developments, existing interfaces to access image collections do not harness the possibilities provided by rich visual details of high-resolution images and detailed tags associated with them. A particularly promising development, however, is the growing research interest in visualization to support the analysis and exploration of cultural heritage data [Windhager et al., 2018]. In this context, art historians are experimenting with digital methods, in particular visualization [Bailey and Pregill, 2014], to explore their potential for expanding the scale and scope of art history [Drucker, 2013; Manovich, 2015]. In these experiments, digital methods tend to be equated with a distanced perspective on the phenomenon [Moretti, 2013] with the result that many visualizations provide high-level overviews that diminish the intricate and intriguing details of individual artifacts [Hochman and Manovich, 2013; Hristova, 2016]. With this research we present an approach towards visualization that is challenging the understanding of overview and detail as something inherently opposed. We introduce a technique that clusters iconographic details of images in order to reveal visual patterns prevalent in a collection.