Modeling visual clutter perception using proto-object segmentation.
J Vis
; 14(7)2014 Jun 05.
Article
em En
| MEDLINE
| ID: mdl-24904121
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Atenção
/
Percepção Visual
/
Simulação por Computador
/
Aglomeração
Tipo de estudo:
Prognostic_studies
Limite:
Adolescent
/
Adult
/
Humans
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article