CLuster: Smart clustering of free-hand sketches on largeinteractive surfaces

Abstract

Structuring and rearranging free-hand sketches on large interactive surfaces typically requires making multiple stroke selections. This can be both time-consuming and fatiguing in the absence of well-designed selection tools. Investigating the concept of automated clustering, we conducted a background study that highlighted the fact that people have varying perspectives on how elements in sketches can and should be grouped. In response to these diverse user expectations, we present cLuster, a flexible, domain-independent clustering approach for free-hand sketches. Our approach is designed to accept an initial user selection, which is then used to calculate a linear combination of pre-trained perspectives in real-time. The remaining elements are then clustered. An initial evaluation revealed that in many cases, only a few corrections were necessary to achieve the desired clustering results. Finally, we demonstrate the utility of our approach in a variety of application scenarios.