  Predicting where people look in natural scenes has attracted a lot of
interest in computer vision and computational neuroscience over the past two
decades. Two seemingly contrasting categories of cues have been proposed to
influence where people look: \textit{low-level image saliency} and
\textit{high-level semantic information}. Our first contribution is to take a
detailed look at these cues to confirm the hypothesis proposed by
Henderson~\cite{henderson1993eye} and Nuthmann \&
Henderson~\cite{nuthmann2010object} that observers tend to look at the center
of objects. We analyzed fixation data for scene free-viewing over 17 observers
on 60 fully annotated images with various types of objects. Images contained
different types of scenes, such as natural scenes, line drawings, and 3D
rendered scenes. Our second contribution is to propose a simple combined model
of low-level saliency and object center-bias that outperforms each individual
component significantly over our data, as well as on the OSIE dataset by Xu et
al.~\cite{xu2014predicting}. The results reconcile saliency with object
center-bias hypotheses and highlight that both types of cues are important in
guiding fixations. Our work opens new directions to understand strategies that
humans use in observing scenes and objects, and demonstrates the construction
of combined models of low-level saliency and high-level object-based
information.
