Saliency Detection using Iterative Dynamic Guided Filtering

Saliency detection is a basic and complex technology in computer vision, which can guide computer to extract key information from image by simulating human visual habit. When image characteristics are unevenly distributed, the accuracy of saliency detection methods will decrease. Unfortunately, this issue is common in natural images and forms a challenge for contrast based methods. We propose an iterative dynamic guided filtering approach to analyze saliency cues. A new and simple kernel function is designed by combining the information of filtering results and input image, which can ensure a good structure transfer from input image to guidance image. The saliency of image pixel is defined based on a novel contrast model using image boundary and center regions. At last, we highlight the result by an exponential function. Experimental results show that the proposed method is superior to the others in terms of detection accuracy and recall rate.