It affects our ability to find things (e.g., Neider & Zelinsky, 2011), how products are marketed and sold to us (Pieters, Wedel, & Zhang, 2007), the efficiency in which we interact with devices (Stone, Fishkin, & Bier, 1994), and even whether we find displays aesthetically pleasing or not (Michailidou, Harper, & Bechhofer, 2008). Whatever definition one chooses, visual clutter is a perception that permeates our lives in an untold number of ways. More operational definitions have also been proposed, defining clutter as “the state in which excess items, or their representation or organization, lead to a degradation of performance at some task” (Rosenholtz, Li, & Nakano, 2007 p. We conclude that the success of the proto-object model is due in part to its use of an intermediate level of visual representation-one between features and objects-and that this is evidence for the potential importance of a proto-object representation in many common visual percepts and tasks.Ĭlutter is defined colloquially as “a crowded or disordered collection of things” ( ). Importantly, we also showed that the proto-object model was a better predictor of clutter perception than an actual count of the number of objects in the scenes, suggesting that the set size of a scene may be better described by proto-objects than objects. We compared the proto-object model to six other models of clutter perception and demonstrated that it outperformed each, in some cases dramatically. We also found that the proto-object model was highly robust to changes in its parameters and was generalizable to unseen images.
Comparing this behaviorally obtained ranking to a ranking based on the model clutter estimates, we found a significant correlation between the two (Spearman's ρ = 0.814, p < 0.001). We tested this model using 90 images of realistic scenes that were ranked by observers from least to most cluttered. Clutter is estimated by simply counting the number of proto-objects.
This unsupervised model segments an image into superpixels, then merges neighboring superpixels that share a common color cluster to obtain proto-objects-defined here as spatially extended regions of coherent features. We introduce the proto-object model of visual clutter perception.
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