  Salient object detection or salient region detection models, diverging from
fixation prediction models, have traditionally been dealing with locating and
segmenting the most salient object or region in a scene. While the notion of
most salient object is sensible when multiple objects exist in a scene, current
datasets for evaluation of saliency detection approaches often have scenes with
only one single object. We introduce three main contributions in this paper:
First, we take an indepth look at the problem of salient object detection by
studying the relationship between where people look in scenes and what they
choose as the most salient object when they are explicitly asked. Based on the
agreement between fixations and saliency judgments, we then suggest that the
most salient object is the one that attracts the highest fraction of fixations.
Second, we provide two new less biased benchmark datasets containing scenes
with multiple objects that challenge existing saliency models. Indeed, we
observed a severe drop in performance of 8 state-of-the-art models on our
datasets (40% to 70%). Third, we propose a very simple yet powerful model based
on superpixels to be used as a baseline for model evaluation and comparison.
While on par with the best models on MSRA-5K dataset, our model wins over other
models on our data highlighting a serious drawback of existing models, which is
convoluting the processes of locating the most salient object and its
segmentation. We also provide a review and statistical analysis of some labeled
scene datasets that can be used for evaluating salient object detection models.
We believe that our work can greatly help remedy the over-fitting of models to
existing biased datasets and opens new venues for future research in this
fast-evolving field.

Computer Vision and Pattern Recognition