Feature Congestion 1.0: A Measure of Visual Clutter

Visual clutter concerns designers of user interfaces and information visualizations. This should not surprise visual perception researchers, since excess and/or disorganized display items can cause crowding, masking, decreased recognition performance due to occlusion, greater difficulty at both segmenting a scene and performing visual search, and so on. Given a reliable measure of the visual clutter in a display, designers could optimize display clutter. Furthermore, a measure of visual clutter could help generalize models like Guided Search (Wolfe, 1994) by providing a substitute for "setsize" more easily computable on more complex and natural imagery. In this paper, we present and test version 1.0 of a measure of visual clutter (Rosenholtz, Li, Mansfield, & Jin, 2005), which operates on arbitrary images as input. This Feature Congestion measure of visual clutter is based on the analogy that a display or scene is more cluttered the more difficult it would be to add a new item which would reliably draw attention. The Statistical Saliency Model (Rosenholtz, 1999, 2001a, 2001b; Rosenholtz & Jin, 2005) for visual search suggests that this difficulty is proportional, locally, to the covariance of certain key features. Version 1.0 of the Feature Congestion measure of visual clutter is based upon three features: color, luminance contrast, and orientation. We explore the use of this measure as a stand-in for set-size in visual search models, and demonstrate that it correlates well with search performance in complex imagery. This includes the search-in-clutter displays of Wolfe, Oliva, Horowitz, Butcher, & Bompas (2002) and Bravo & Farid (2004), as well as images like baggage checkpoint x-ray images.