A PFT Visual Attention Detection Model Using Bayesian Framework
Chaoke Pei, Li Fu Gao, Donghui Wang, Ying Hong · 2011
Visual attention refers to the perceptual quality that makes an object or a region pop out relative to its neighbors and seize human's visual attention. Recently, a new fast approach based on phase spectrum of Fourier Transform (PFT) was proved to be effective and also parameter-free. In this paper, we present a novel improved saliency detection model using PFT as well as the Bayesian framework. The bottom-up saliency is gathered based on PFT in several color channels and the Bayesian framework is used to incorporate top-down information with this bottom-up saliency. Experiments show that our fast PFT-based Bayesian model achieves better and more robust results than that from the state-of-the-art.