Global Contrast based Salient Region Detection (Ming ming Cheng)

标签：论文 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:基于直方图对比度的方法，每一个像素的显著性值是由它与图像中所有其他像素的颜色差异来确定的，得到全分辨率显著性图像；

（2）RC：基于局部对比度的方法，先将图像分割成小区域，采用的分割方法是基于图的分割，基本分割思想是将每一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，要求连接相同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它所有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 距离最近的颜色代替。

2、颜色空间平滑：减小量化误差，每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。

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Global Contrast based Salient Region Detection (Ming ming Cheng)

标签：论文 cvpr

原文地址：http://blog.csdn.net/u013476464/article/details/34108201