title: Modeling visual clutter perception using proto-object segmentation.
abstract: We introduce the proto-object model of visual clutter perception. 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. Clutter is estimated by simply counting the number of proto-objects. We tested this model using 90 images of realistic scenes that were ranked by observers from least to most cluttered. 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 also found that the proto-object model was highly robust to changes in its parameters and was generalizable to unseen images. We compared the proto-object model to six other models of clutter perception and demonstrated that it outperformed each, in some cases dramatically. 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 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.
authors.affiliation: Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.
authors.forename: Chen-Ping
authors.initials: CP
authors.lastname: Yu
authors.affiliation: Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.
authors.forename: Dimitris
authors.initials: D
authors.lastname: Samaras
authors.affiliation: Department of Computer Science, Stony Brook University, Stony Brook, NY, USADepartment of Psychology, Stony Brook University, Stony Brook, NY, USA.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Zelinsky
mesh_terms: D000293:Adolescent; D000328:Adult; D001288:Attention / Q000502:physiology*; D003198:Computer Simulation; D003441:Crowding; D005133:Eye Movements / Q000502:physiology; D006801:Humans; D014796:Visual Perception / Q000502:physiology*; D055815:Young Adult
keywords: color clustering; image segmentation; midlevel visual representation; proto-objects; superpixel merging; visual clutter