Exploiting global rarity, local contrast and central bias for salient region learning
We build a saliency model combining global rarity, local contrast and central bias. We use the region information towards highlighting the whole object. We take a neural network based learning approach to combine the three factors. Our model outperforms 11 representative models to detect salient objects. Our model also well predicts eye fixations compared with eight classic models.
global rarity local contrast central bias salient region learning