Previous work on predicting the target of visual search from human
fixations only considered closed-world settings in which training labels
are available and predictions are performed for a known set of potential
targets. In this work we go beyond the state of the art by studying search
target prediction in an open-world setting in which we no longer assume
that we have fixation data to train for the search targets. We present a
dataset containing fixation data of 18 users searching for natural images
from three image categories within synthesised image collages of about 80
images. In a closed-world baseline experiment we show that we can predict
the correct target image out of a candidate set of five images. We then
present a new problem formulation for search target prediction in the
open-world setting that is based on learning compatibilities between
fixations and potential targets.
http://perceptual.mpi-inf.mpg.de/files/2015/04/sattar15_cvpr.pdf