title: A computational visual saliency model based on statistics and machine learning.
abstract: Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases.
authors.affiliation: Department of Electrical Engineering, National Taiwan University, Taiwan.
authors.forename: Ru-Je
authors.initials: RJ
authors.lastname: Lin
authors.affiliation: Department of Electrical Engineering, National Taiwan University, Taiwan.
authors.forename: Wei-Song
authors.initials: WS
authors.lastname: Lin
mesh_terms: D001185:Artificial Intelligence; D001288:Attention / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008433:Mathematics; D014796:Visual Perception / Q000502:physiology*
keywords: Bayesian theory; center bias; information theory; selective attention; support vector regression; visual saliency