Abstract Digital image collections contain a wealth of information, which for instance can be used to trace illegal activities and investigate criminal networks. We present a method that enables analysts to reveal relations among people, based on the patterns in their collections. Similar temporal and spatial patterns can be found using a parameterized algorithm, visualization is used to choose the right parameters and to inspect the patterns found. The visualization shows relations between image properties: the person it belongs to, the concepts in the image, its time stamp and location. We demonstrate the method with image collections of 10, 000 people containing 460, 000 images in total.

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.

Abstract The exploration of high‐dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an IF‐Table, showing all items with a selection of features; and an FI‐Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.

Abstract Medicine prescriptions play an important role in medical treatments. More insight in medicine prescription behavior can lead to more efficient and effective treatments, as well as reflection on prescription behavior for specific physicians, types of medicines, or classes of patients. Most current medical visualization systems show health data only from the perspective of patients, whereas to understand prescription behavior multiple perspectives are relevant. We present a new approach to visualize prescription data from four different perspectives: physician, patient, medicine, and prescription. Information about physicians, patients, and medicines is shown in three tables; relations between selected items in these tables are shown using custom glyphs and histograms. These tables can also be used to define selections of prescriptions which can be compared to each other by showing a variety of metrics. This enables physicians and possibly other stakeholders to perform a wide variety of queries and inspections, while the use of familiar metaphors, such as tables and histograms, enables them to use the system in short time. This was confirmed by an evaluation session with six neurologists from an institute of epileptology. Our system is tailored to medicine prescription data, but we argue that the underlying pattern in the data is ubiquitous, and that hence our approach can be useful for many other cases where A provides B to C.

A common task in visualization is to quickly find interesting items in large sets. When appropriate metadata is missing, automatic queries are impossible and users have to inspect all elements visually. We compared two fundamentally different, but obvious display modes for this task and investigated the difference with respect to effectiveness, efficiency, and satisfaction. The static mode is based on the page metaphor and presents successive pages with a static grid of items. The moving mode is based on the conveyor belt metaphor and lets a grid of items slide though the screen in a continuous flow. In our evaluation, we applied both modes to the common task of browsing images. We performed two experiments where 18 participants had to search for certain target images in a large image collection. The number of shown images per second (pace) was predefined in the first experiment, and under user control in the second one. We conclude that at a fixed pace, the mode has no significant impact on the recall. The perceived pace is generally slower for moving mode, which causes users to systematically choose for a faster real pace than in static mode at the cost of recall, keeping the average number of target images found per second equal for both modes.