Oliva, Mack, Shrestha, and Peeper (
2004) have attempted to determine what factors influence the human representation of “complexity,” a concept clearly related to that of clutter. They had users hierarchically sort photographs according to their complexity and indicate at each hierarchical level the basis for the sort. Users indicated that complexity depended on the quantity and variety of objects, detail, and color, as well as on higher level, more global concepts like the symmetry, organization, and “openness” of the depicted space. Our Feature Congestion and Subband Entropy measures of visual clutter, as we will see, correlate with quantity and variety of objects, although we do not explicitly find objects. Our Feature Congestion measure explicitly incorporates a notion of the variety of color in an image, and the Subband Entropy measure also captures this, because more color variability means less predictability, and thus, more information = entropy = clutter. Both measures will implicitly deal with certain aspects of perceptual organization, such as grouping by a combination of proximity and similarity. In a later version of the Feature Congestion measure, we are interested in incorporating some of the higher level components of perceptual organization that Oliva et al. suggest. Mack and Oliva (
2004) have implemented early versions of several measures of complexity: quantity of contours, degree of symmetry, global color variability, and degree of openness. (We argue that local color variability is more appropriate because it implicitly responds to local groupings of color. Local variability has been shown to affect search performance; e.g., Nothdurft,
1993.) They compared these measures to the mean subjective rankings of complexity on indoor scenes and found a correlation of
r = .85. Edge density—the percentage of pixels that are edge pixels—alone led to a correlation with mean subjective rankings of
r = .83. (Note, however, that the mean Spearman rank-order correlation between subjects was only
r = .61, comparable with what we have found for subjective judgments of clutter in maps; Rosenholtz, Li, Mansfield, & Jin,
2005;
r = .70. In that article, we found a correlation of
r = .77 between median subjective judgments of clutter in maps and an earlier version of the Feature Congestion clutter measure. None of these differences in correlation coefficients is significant,
p > .05.) This high correlation between subjective judgments of complexity in indoor scenes and such a simple measure as edge density suggests that this simple measure is worth examining further. In what follows, we also examine the performance of an Edge Density measure of visual clutter. To obtain the Edge Density measure for each image, we applied MATLAB's Canny edge detector to each image and measured the density of edge pixels. The Canny edge detector has several parameters: a low threshold, high threshold, and sigma. These parameters were set by hand to values that gave good results overall to the examples presented in this article. The low threshold and high threshold are used to find weak and strong edges, respectively, and the Canny edge detector keeps weak edges only if they are connected to strong edges. These thresholds were set to 0.11 and 0.27, respectively. The sigma parameter is the standard deviation of the Gaussian filter used in the computation of the gradient. It was set to the default,
σ = 1, comparable with the finest scale in the Feature Congestion and Subband Entropy measures.