Measuring visual clutter.

By Ruth Rosenholtz, Yuanzhen Li, L. Nakano. Journal of Vision, 2007

863 citations recorded by the source corpus.

Visual clutter concerns designers of user interfaces and information visualizations. This should not surprise visual perception researchers because 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 (J. M. Wolfe, 1994) by providing a substitute for "set size" more easily computable on more complex and natural imagery. In this article, we present and test several measures of visual clutter, which operate on arbitrary images as input. The first is a new version of the Feature Congestion measure of visual clutter presented in R. Rosenholtz, Y. Li, S. Mansfield, and Z. Jin (2005). This Feature Congestion measure of visual clutter is based on the analogy that the more cluttered a display or scene is, the more difficult it would be to add a new item that would reliably draw attention. A second measure of visual clutter, Subband Entropy, is based on the notion that clutter is related to the visual information in the display. Finally, we test a third measure, Edge Density, used by M. L. Mack and A. Oliva (2004) as a measure of subjective visual complexity. We explore the use of these measures as stand-ins for set size in visual search models and demonstrate that they correlate well with search performance in complex imagery. This includes the search-in-clutter displays of J. M. Wolfe, A. Oliva, T. S. Horowitz, S. Butcher, and A. Bompas (2002) and Bravo and Farid (2004), as well as new search experiments. An additional experiment suggests that color variability, accounted for by Feature Congestion but not the Edge Density measure or the Subband Entropy measure, does matter for visual clutter.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c8318039383ab2963c3b7778db5f71fcb4a00f32

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Citations in the corpus

The Cognitive Science of Visual-Spatial Displays: Implications for Design (2011, background): “…in the display relieves the user of the need to maintain a detailed representation of this information in working memory, whereas presenting too much information in the display leads to visual clutter or distraction by irrelevant information (Rosenholtz et al., 2007; Wickens & Carswell, 1995).”
Perceptual issues in augmented reality revisited (2010, background): “A key problem associated with structure is clutter , in which excess items lead to a degradation in task performance (Rosenholtz et al. [49]).”
Visual search: a retrospective. (2011, background): “Visual clutter, which entails crowding, masking, decreased recognition performance due to occlusion, and greater difﬁculty at segmenting a scene, also affects visual search (Rosenholtz, Li, & Nakano, 2007).”
Modeling Search for People in 900 Scenes: A combined source model of eye guidance (2009, positive): “Indeed, rare image features in an image are more likely to be diagnostic of objects (Elazary & Itti, 2008; Torralba et al., 2006), whereas repetitive image features or large homogenous regions are unlikely to be object-like (Bravo & Farid, 2006; Rosenholtz, Li, & Nakano, 2007 ).”
Eye Tracking and Nutrition Label Use: A Review of the Literature and Recommendations for Label Enhancement (2012, background): “Moreover, specific features of food packages contribute to the amount of perceived clutter and, consequently, consumer attention, such as the variability of colors and the contrast between the target stimulus and its background (Rosenholtz et al., 2007).”
What do eyes reveal about the mind?: Algorithmic inference of search targets from fixations (2015, background): “Those factors include global scene context [79], background clutter [73], object-semantic dependencies [37], and spatial priors [63].”
State-of-the-Art in Visual Attention Modeling (2013, background): “Some studies have also added specific features for directing attention like skin hue [120], face [167], horizontal line [93], wavelet [133], gist [92], [93], center-bias [123], curvature [124], spatial resolution [125], optical flow [15], [126], flicker [119], multiple superimposed orientations (cro”
Pros and Cons of GAN Evaluation Measures (2018, background): “For example, understanding how humans perceive symmetry [19, 26] or image clutter [67] in generated images versus natural scenes can give clues regarding the plausibility of the generated images.”
Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness (2013, background): “Clutter, in her definition, increases in tandem to the number of unusual objects human attention is drawn to—a concept that is partially related to visual complexity [30].”