  Omni-directional images have been used in wide range of applications. For the
applications, it would be useful to estimate saliency maps representing
probability distributions of gazing points with a head-mounted display, to
detect important regions in the omni-directional images. This paper proposes a
novel saliency-map estimation model for the omni-directional images by
extracting overlapping 2-dimensional (2D) plane images from omni-directional
images at various directions and angles of view. While 2D saliency maps tend to
have high probability at the center of images (center bias), the
high-probability region appears at horizontal directions in omni-directional
saliency maps when a head-mounted display is used (equator bias). Therefore,
the 2D saliency model with a center-bias layer was fine-tuned with an
omni-directional dataset by replacing the center-bias layer to an equator-bias
layer conditioned on the elevation angle for the extraction of the 2D plane
image. The limited availability of omni-directional images in saliency datasets
can be compensated by using the well-established 2D saliency model pretrained
by a large number of training images with the ground truth of 2D saliency maps.
In addition, this paper proposes a multi-scale estimation method by extracting
2D images in multiple angles of view to detect objects of various sizes with
variable receptive fields. The saliency maps estimated from the multiple angles
of view were integrated by using pixel-wise attention weights calculated in an
integration layer for weighting the optimal scale to each object. The proposed
method was evaluated using a publicly available dataset with evaluation metrics
for omni-directional saliency maps. It was confirmed that the accuracy of the
saliency maps was improved by the proposed method.
