ICLIC: Interactive categorization of large image collections

Abstract

We present a new approach for the analysis of large image collections. We argue that categorization plays an important role in this process, not only to label images as end result, but also during exploration. Furthermore, to increase the effectiveness and efficiency of the categorization process we enable the use of all available metadata, treated as multivariate data. We identified images, attributes, and categories as important aspects and integrated these in a system called ICLIC. The system consists of four views that are connected by the selection of images, which is a central action in the approach. By using a minimalist interface with only standard metaphors, users are enabled to use the system in short time. The system enables complex queries in a natural way, and it can deal with collections containing more than 100,000 images and more than 1,000 metadata attributes. This was confirmed by two evaluation cycles with domain experts.