基于条件随机场和图像分割的显著性检测

Saliency Detection Based on Conditional Random Field and Image Segmentation

针对当前常见的显著性方法检测得到的显著性区域边界稀疏不明确、内部不均匀致密等问题,提出了一种基于条件随机场(Condition random field,CRF)和图像分割的显著性检测方法.该方法综合利用边界信息、局部信息以及全局信息,从图像中提取出多种显著性特征;在条件随机场框架下融合这些特征,通过显著性区域与背景区域的区域标注实现显著性区域的粗糙检测;结合区域标注结果和交互式图像分割方法实现显著性区域的精确检测.实验结果表明本文提出的方法能够清晰而准确地提取出图像中的显著性区域,有效提高显著性检测精度.

The problem of sparse and unclear boundary with uneven and non-compact interior existed in the saliency region detected by most saliency detection methods. In order to solve this problem, this paper proposes a saliency detection method based on the conditional random field （CRF） and image segmentation. This method comprehensively utilizes boundary information, local information and global information to extract a variety of salient features from an image. By fusing these features into the framework of conditional random field, a coarse detection for saliency region is realized based on region labeled of saliency region and background region, and then a fine detection for saliency region is achieved through combining the result of region labeled with an interactive image segmentation method. Experimental results show that the proposed approach can clearly and accurately extract saliency regions and improve the detection precision.