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:基于直方图对照度的方法，每个像素的显著性值是由它与图像中全部其它像素的颜色差异来确定的，得到全分辨率显著性图像；

（2）RC：基于局部对照度的方法。先将图像切割成小区域。採用的切割方法是基于图的切割，基本切割思想是将每个像素点作为无向图的顶点，两个像素点之间的不相似度作为边的权重。要求连接同样区域内的两个顶点的边的最大权重要小于连接不同区域的变得最小权重，在迭代过程中进行顶点归纳与区域合并，每个区域的显著性值由它与其他全部区域的空间距离和区域像素数加权的颜色差异来确定；空间距离为两个区域重心的欧式距离。较远的区域分配较小的权值。

（3）加速细节：

1、将每一个颜色通道由256个颜色值量化到12个颜色值后，对输入颜色图像计算颜色直方图。保留高频颜色，剩下的颜色舍弃。用直方图中 距离近期的颜色取代。

2、颜色空间平滑：减小量化误差。每一个颜色的显著性值被替换为相似颜色显著性的加权平均。在RGB空间进行量化。用Lab空间度量距离。

接下来会进行代码解析。

详细内容參考：点击打开链接

版权声明：本文博客原创文章，博客，未经同意，不得转载。