影像顯著性偵測(image saliency detection)主要是將影像中相對顯著的部分突出,並以影像顯著圖(saliency map)呈現出來。然而,有些影像的顯著圖不明顯,無法有效地呈現且分別出景物。而暗顏色先驗(dark channel prior, DCP)則可還原出清晰顯著圖。 基於這樣互補性質的觀察,我們的研究首先將暗顏色先驗應用在影像顯著性偵測中,使低對比度顯著圖的影像能夠成功突出景物。另外,我們還提出一個簡單的顯著性切割方法,先將影像顯著圖統計到直方圖上,並且做一次方平滑處理;再來,主要基於基準線我們選出一個最深且最寬的谷做為切割點;最後,將高於切割閥值的部分定義為前景、其他則為背景。在影像顯著圖的部份,相較於原本的結果,我們的實驗結果有明顯的改善。在最後影像顯著閥值挑選的部分,用了一個簡單的方法切割了影像,並且也有不錯的結果。
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.