Efficient Construction of Saliency Map

摘要

The saliency map is useful for many applications such as image compression, display, and visualization. However, the bottom-up model used in most saliency map construction methods is computationally expensive. The purpose of this paper is to improve the efficiency of the model for automatic construction of the saliency map of an image while preserving its accuracy. In particular, we remove the contrast sensitivity function and the visual masking component of the bottom-up visual attention model and retain the components related to perceptual decomposition and center-surround interaction that are critical properties of human visual system. The simplified model is verified by performance comparison with the ground truth. In addition, a salient region enhancement technique is adopted to enhance the connectivity of the saliency map, and the saliency maps of three color channels are fused to enhance the prediction accuracy. Experimental results show that the average correlation between our algorithm and the ground truth is close to that between the original model and the ground truth, while the computational complexity is reduced by 98%.