= PDF Reprint,     = BibTeX entry,     = Online Abstract

Click to download PDF version Click to download BibTeX data Clik to view abstract L. Elazary, L. Itti, Interesting objects are visually salient, Journal of Vision, Vol. 8, No. 3:3, pp. 1-15, Mar 2008. [2006 impact factor: 3.753] (Cited by 246)

Abstract: 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.

Themes: Model of Top-Down Attentional Modulation, Computational Modeling, Computer Vision, Scene Understanding


Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
This page generated by bibTOhtml on Wed Feb 15 12:13:56 PST 2017