Global Contrast based Salient Region Detection (Ming ming Cheng)

abstract：
Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object extraction algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut for high quality salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

（1）HC:基於直方圖對比度的方法，每一個畫素的顯著性值是由它與影象中所有其他畫素的顏色差異來確定的，得到全解析度顯著性影象；

（2）RC：基於區域性對比度的方法，先將影象分割成小區域，採用的分割方法是基於圖的分割，基本分割思想是將每一個畫素點作為無向圖的頂點，兩個畫素點之間的不相似度作為邊的權重，要求連線相同區域內的兩個頂點的邊的最大權重要小於連線不同區域的變得最小權重，在迭代過程中進行頂點歸納與區域合併，每個區域的顯著性值由它與其它所有區域的空間距離和區域畫素數加權的顏色差異來確定；空間距離為兩個區域重心的歐式距離，較遠的區域分配較小的權值。

（3）加速細節：

1、將每個顏色通道由256個顏色值量化到12個顏色值後，對輸入顏色影象計算顏色直方圖，保留高頻顏色，剩下的顏色捨棄，用直方圖中 距離最近的顏色代替。

2、顏色空間平滑：減小量化誤差，每個顏色的顯著性值被替換為相似顏色顯著性的加權平均；在RGB空間進行量化，用Lab空間度量距離。

 

 

接下來會進行程式碼解析。具體內容參考：點選開啟連結