Segmentation-enhanced saliency detection model based on distance transform and center bias
Saliency detection is one of the extraordinary abilities of the human visual system (HVS), and also provides a powerful tool for predicting where humans tend to focus in the free-viewing process. In this paper, we propose a novel method for computing image saliency. At first, an image is subject to L0 smoothing to characterize its fundamental constituents while diminishing insignificant details. Distance-transform-based saliency detection is then applied to the smoothed image, to extract the general salient regions and form a rough saliency map. Next, the segmentation information generated by normalized cuts is used to improve the saliency detection performance by averaging the saliency values in each segmented block. Finally, we employ the center-bias mechanism to further improve the saliency model. The proposed method is compared with six existing saliency models, and achieves the best performance in terms of the area under the ROC curve (AUC).