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It has become apparent that a Gaussian center bias can serve as an importantprior for visual saliency detection, which has been demonstrated for predictinghuman eye fixations and salient object detection. Tseng et al. have shown thatthe photographer's tendency to place interesting objects in the center is alikely cause for the center bias of eye fixations. We investigate the influenceof the photographer's center bias on salient object detection, extending ourprevious work. We show that the centroid locations of salient objects inphotographs of Achanta and Liu's data set in fact correlate strongly with aGaussian model. This is an important insight, because it provides an empiricalmotivation and justification for the integration of such a center bias insalient object detection algorithms and helps to understand why Gaussian modelsare so effective. To assess the influence of the center bias on salient objectdetection, we integrate an explicit Gaussian center bias model into twostate-of-the-art salient object detection algorithms. This way, first, wequantify the influence of the Gaussian center bias on pixel- and segment-basedsalient object detection. Second, we improve the performance in terms of F1score, Fb score, area under the recall-precision curve, area under the receiveroperating characteristic curve, and hit-rate on the well-known data set byAchanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, weexemplarily demonstrate that implicit center biases are partially responsiblefor the outstanding performance of state-of-the-art algorithms. Last but notleast, as a result of debiasing Cheng et al.'s algorithm, we introduce anon-biased salient object detection method, which is of interest forapplications in which the image data is not likely to have a photographer'scenter bias (e.g., image data of surveillance cameras or autonomous robots).

On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection