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Ruth Rosenholtz, Yuanzhen Li, Zhenlan Jin, Jonathan Mansfield; Feature congestion: A measure of visual clutter. Journal of Vision 2006;6(6):827. doi: 10.1167/6.6.827.
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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 “set size” more easily computable on more complex and natural imagery.
We present a first cut of a measure of visual clutter (Rosenholtz et al, SIGCHI 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. Our Statistical Saliency Model for visual search suggests that this difficulty is proportional, locally, to the covariance of certain key features.
We demonstrate that this measure correlates well with subjective assessments of visual clutter for a wide range of stimuli, and also that it correlates well with search performance in complex imagery, particularly on target-absent trials. This includes the search-in-clutter displays of Wolfe et al (2002) and Bravo & Farid (2004). We explore the use of this measure as a stand-in for setsize in visual search models.
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