Style-based exploration of illustration datasets

Searching by style in illustration data sets is a particular problem in Information Retrieval which has received little attention so far. One of its main problems is that the perception of style is highly subjective, which makes labeling styles a very difficult task. Despite being difficult to predict computationally, certain properties such as colorfulness, line style or shading can be successfully captured by existing style metrics. However, there is little knowledge about how we distinguish between different styles and how these metrics can be used to guide users in style-based interactions. In this paper, we propose several contributions towards a better comprehension of illustration style and its usefulness for data exploration and retrieval. First, we provide new insights about how we perceive style in illustration. Second, we evaluate a handmade style clustering of clip art pieces with an existing style metric to analyze how this metric aligns with expert knowledge. Finally, we propose a method for efficient navigation and exploration of large clip art data sets which takes into account both semantic labeling of the data and its style. Our approach combines hierarchical clustering with dimensionality reduction techniques, and strategic sampling to obtain intuitive visualizations and useful visualizations.