Saliency Map Estimation for Omni-Directional Image Considering Prior Distributions

TL;DR

This paper introduces novel deep learning methods for estimating saliency maps in omni-directional images, considering prior fixation distributions, filling a gap in virtual environment applications.

Contribution

It proposes new saliency map estimation techniques specifically designed for omni-directional images, incorporating prior distribution properties.

Findings

Effective saliency map estimation for omni-directional images.

Incorporation of prior fixation distribution improves accuracy.

Addresses a previously unexplored area in virtual environment analysis.

Abstract

In recent years, the deep learning techniques have been applied to the estimation of saliency maps, which represent probability density functions of fixations when people look at the images. Although the methods of saliency-map estimation have been actively studied for 2-dimensional planer images, the methods for omni-directional images to be utilized in virtual environments had not been studied, until a competition of saliency-map estimation for the omni-directional images was held in ICME2017. In this paper, novel methods for estimating saliency maps for the omni-directional images are proposed considering the properties of prior distributions for fixations in the planar images and the omni-directional images.