Visualising image databases

In this paper we explore different ways in which large collections of images can be visualised. We discuss the three principle visualisation techniques employed for this purpose, namely dimensionality reduced mappings, clustering-based visualisations and graph-based representations. Mapping-based techniques try to present the relationships between images described by high-dimensional features in a low-dimensional visualisation space. Clustered visualisations group similar images based on content, metadata or time stamp information, while in graph-based approaches links between images are exploited to arrive at an intuitive display of the dataset. We highlight advantages and disadvantages of the various approaches and emphasise the need for a benchmark which allows objective evaluation of these systems.