  This paper presents a novel fixation prediction and saliency modeling
framework based on inter-image similarities and ensemble of Extreme Learning
Machines (ELM). The proposed framework is inspired by two observations, 1) the
contextual information of a scene along with low-level visual cues modulates
attention, 2) the influence of scene memorability on eye movement patterns
caused by the resemblance of a scene to a former visual experience. Motivated
by such observations, we develop a framework that estimates the saliency of a
given image using an ensemble of extreme learners, each trained on an image
similar to the input image. That is, after retrieving a set of similar images
for a given image, a saliency predictor is learnt from each of the images in
the retrieved image set using an ELM, resulting in an ensemble. The saliency of
the given image is then measured in terms of the mean of predicted saliency
value by the ensemble's members.
