Interesting objects are visually salient.
How do we decide which objects in a visual scene are more interesting? While intuition may point toward high-level object recognition and cognitive processes, here we investigate the contributions of a much simpler process, low-level visual saliency. We used the LabelMe database (24,863 photographs with 74,454 manually outlined objects) to evaluate how often interesting objects were among the few most salient locations predicted by a computational model of bottom-up attention. In 43% of all images the model's predicted most salient location falls within a labeled region (chance 21%). Furthermore, in 76% of the images (chance 43%), one or more of the top three salient locations fell on an outlined object, with performance leveling off after six predicted locations. The bottom-up attention model has neither notion of object nor notion of semantic relevance. Hence, our results indicate that selecting interesting objects in a scene is largely constrained by low-level visual properties rather than solely determined by higher cognitive processes.
Elazary
Lior
L
Department of Computer Science, University of Southern California, Los Angeles, CA, USA. elazary@usc.edu
Itti
Laurent
L
D001288:Attention / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology; D006801:Humans; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D010781:Photography; D011930:Reaction Time; D017603:Signal Detection, Psychological / Q000502:physiology*; D013997:Time Factors