Global Contrast based Salient Region Detection (Ming ming Cheng)

標籤：論文 cvpr

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:基於長條圖對比的方法，每一個像素的顯著性值是由它與映像中所有其他像素的顏色差異來確定的，得到全解析度顯著性映像；

（2）RC：基於局部對比的方法，先將映像分割成小地區，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像素點作為無向圖的頂點，兩個像素點之間的不相似度作為邊的權重，要求串連相同地區內的兩個頂點的邊的最大權重要小於串連不同地區的變得最小權重，在迭代過程中進行頂點歸納與地區合并，每個地區的顯著性值由它與其它所有地區的空間距離和地區像素數加權的顏色差異來確定；空間距離為兩個地區重心的歐式距離，較遠的地區分配較小的權值。

（3）加速細節：

1、將每個色彩通道由256個顏色值量化到12個顏色值後，對輸入顏色映像計算顏色長條圖，保留高頻顏色，剩下的顏色捨棄，用長條圖中 距離最近的顏色代替。

2、色彩空間平滑：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。

 

 

接下來會進行代碼解析。具體內容參考：點擊開啟連結