A survey of computational methods for iconic image analysis

Abstract

Abstract Digitization and digitalization efforts have led to an explosive growth of the number of images that are published, shared, and made available in collections. In turn, this has resulted in increased awareness of, and interest in, computational methods for automatic image analysis. Despite the tremendous progress made in the development of computational methods, there remains a gap between how a person interprets an image and what can be automatically extracted. By considering iconic images as those images for which this gap is most salient, as their meaning goes well beyond what is represented in the visual data, this article gives an overview of the potential and limitations of computational methods for iconic image analysis. I structure this overview by discussing methods that can be used to analyse the production, distribution, and reception of iconic images. Although the majority of computational methods focus on analysing production aspects, there are promising methods for image distribution aspects, whereas methods for studying image reception have received little attention. By considering the limitations of available methods I argue that computational methods can be of use for studying iconic images, but that comprehensive analysis will require methods that incorporate the plurality of meanings an image can have, and temporal nature thereof.