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Lior Elazary, Laurent Itti; Interesting objects are visually salient. Journal of Vision 2008;8(3):3. doi: https://doi.org/10.1167/8.3.3.
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© ARVO (1962-2015); The Authors (2016-present)
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.
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