An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based visual attention analysis with its application in image browsing on small displays
Proceedings of the 15th international conference on Multimedia
From Gestalt Theory to Image Analysis: A Probabilistic Approach
From Gestalt Theory to Image Analysis: A Probabilistic Approach
Visual attention analysis by pseudo gravitational field
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Fast and efficient saliency detection using sparse sampling and kernel density estimation
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Learning visual saliency based on object's relative relationship
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
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Visual attention is an important issue in image and video analysis and keeps being an open problem in the computer vision field. Motivated by the famous Helmholtz principle, a new approach of visual attention analysis is proposed in this paper based on the low level feature statistics of natural images and the Bayesian framework. Firstly, two priors, i.e., Surrounding Feature Prior (SFP) and Single Feature Probability Distribution (SFPD) are learned and integrated by a Bayesian framework to compute the chance of happening (CoH) of each pixel in an image. Then another prior, i.e., Center Bias Prior (CBP), is learned and applied to the CoH to compute the saliency map of the image. The experimental results demonstrate that the proposed approach is both effective and efficient by providing more accurate and quick visual attention location. We make three major contributions in this paper: (1) A set of simple but powerful priors, SFP, SFPD and CBP, are presented in an intuitive way; (2) A computational model of CoH based on Bayesian framework is given to integrate SFP and SFPD together; (3) A computationally plausible way to obtain the saliency map of natural images based on CoH and CBP.