On creating a 2D & 3D visual saliency dataset
Zbigniew Zdziarski, Rozenn Dahyot · 2013
Visual saliency (VS), which refers to the study of the behaviour or perception of the human vision system, is usually captured using eye tracking technologies on a (statistically) representative set of participants watching visual media on a screen. Using eye tracking technologies to capture the visual behaviour of a set of candidates is a long, expensive and tedious experiment to set. Artificial intelligence can be used to replicate this human behaviour and several 2D and 3D visual saliency algorithms (VSAs) have been proposed [Wang et al. 2013; Zdziarski and Dahyot 2013]. These algorithms attempt to reproduce human visual perception behaviour and have assisted in applications such as video content creation, retargetting and summarisation. Automatically replicating viewing behaviour is easier, less cumbersome and cheaper than the manual option presented by eye trackers.