title: Intrinsic dimensionality predicts the saliency of natural dynamic scenes.
abstract: Since visual attention-based computer vision applications have gained popularity, ever more complex, biologically inspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatiotemporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labeling scenarios.
authors.affiliation: Institute for Neuro- and Bioinformatics, University of Lübeck, Ratzeburger Allee 160, Lübeck D-23538, Germany. vig@inb.uni-luebeck.de
authors.forename: Eleonora
authors.initials: E
authors.lastname: Vig
authors.forename: Michael
authors.initials: M
authors.lastname: Dorr
authors.forename: Thomas
authors.initials: T
authors.lastname: Martinetz
authors.forename: Erhardt
authors.initials: E
authors.lastname: Barth
mesh_terms: D000465:Algorithms; D005133:Eye Movements / Q000502:physiology; D006801:Humans; D010364:Pattern Recognition, Visual; D025341:Principal Component Analysis; D014741:Video Recording; D014785:Vision, Ocular / Q000502:physiology*; D014796:Visual Perception