Modeling Clutter Perception using Parametric Proto-object Partitioning
Chen-Ping Yu, Wen‐Yu Hua, Dimitris Samaras, Greg Zelinsky · 2013
Visual clutter, the perception of an image as being crowded and disordered, af-fects aspects of our lives ranging from object detection to aesthetics, yet relatively little effort has been made to model this important and ubiquitous percept. Our approach models clutter as the number of proto-objects segmented from an im-age, with proto-objects defined as groupings of superpixels that are similar in intensity, color, and gradient orientation features. We introduce a novel paramet-ric method of clustering superpixels by modeling mixture of Weibulls on Earth Mover’s Distance statistics, then taking the normalized number of proto-objects following partitioning as our estimate of clutter perception. We validated this model using a new 90-image dataset of real world scenes rank ordered by human raters for clutter, and showed that our method not only predicted clutter extremely well (Spearman’s ρ = 0.8038, p < 0.001), but also outperformed all existing clut-ter perception models and even a behavioral object segmentation ground truth. We conclude that the number of proto-objects in an image affects clutter perception more than the number of objects or features. 1