Abstract
Image saliency detection is a process to pop out the most salient part in the image, and shows up with image saliency map. However, some image saliency maps are not accurate enough to separate foreground and background from images with low contrast; dark channel prior (DCP) can transform these image into a clear image. In this paper, we first apply DCP in image saliency detection to emphasize foreground from image with low contrast saliency. Moreover, we propose a simple cutting method on image saliency. We convert the saliency map into a histogram and use a first degree polynomial to smooth the histogram. The deepest and widest valley of the smoothed histogram is chosen as the cutting threshold. The part higher than threshold is identified as foreground, and the other is background. In our experiment, it proves that the proposed method successfully segments the foreground and background from the image.