A probabilistic saliency model with memory-guided top-down cues for free-viewing

Attention models for free-viewing of images are commonly developed in a bottom-up (BU) manner. However, in this paper, we propose to include memory-oriented top-down spatial attention cues into the model. The proposed generative saliency model probabilistically combines a BU module and a top-down (TD) module and can be applied to both static and dynamic scenes. For static scenes, the experience of attention distribution to similar scenes in long-term memory is mimicked by linearly mapping a global feature of the scene to the long-term top-down (LTD) saliency. And for dynamic scenes, we add on the influence of short-term memory to form a HMM-like chain to guide the attention distribution. Our improved BU module utilizes low-level feature contrast, spatial distribution and location information to highlight saliency regions. It is tested on 1000 benchmark images and outperforms state-of-the-art BU methods. The complete model is examined on two video datasets, one with manually labeled saliency regions, and another with recorded eye-movement fixations. Experimental results show that our model achieves significant improvement in predicting human visual attention compared with existing saliency models.