Visual saliency as sequential eye fixation probability

Human vision system acquires essential information from the environment by sequentially sampling visual contents at important locations under the control of selective attention mechanism. We propose that bottom-up saliency is not based on global statistics but on information sampled at prior eye fixations. Our model calculates visual saliency using sequential eye fixation probability. However, the proposed model needs fixation priors, which are hard to simulate given current fixation data and experimental conditions. An approximation is proposed to generate a single saliency map by fusing all possible conditions of fixation prior. Our method outperforms all state-of-the-art models in predicting eye fixations, and shows reasonable response to various psychological patterns.