This paper addresses the issue of robust background detection and saliency optimization in salient object detection. The authors propose a novel background measure called "boundary connectivity," which characterizes the spatial layout of image regions relative to image boundaries, providing a more robust and intuitive interpretation compared to previous methods. They also introduce a principled optimization framework that integrates multiple low-level cues, including the proposed background measure, to generate clean and uniform saliency maps. The framework is efficient and achieves state-of-the-art results on several benchmark datasets. The paper discusses the advantages of the proposed method, including its robustness to image appearance variations and its ability to handle pure background images. Experimental results on various datasets demonstrate the effectiveness and superiority of the proposed approach over existing methods.This paper addresses the issue of robust background detection and saliency optimization in salient object detection. The authors propose a novel background measure called "boundary connectivity," which characterizes the spatial layout of image regions relative to image boundaries, providing a more robust and intuitive interpretation compared to previous methods. They also introduce a principled optimization framework that integrates multiple low-level cues, including the proposed background measure, to generate clean and uniform saliency maps. The framework is efficient and achieves state-of-the-art results on several benchmark datasets. The paper discusses the advantages of the proposed method, including its robustness to image appearance variations and its ability to handle pure background images. Experimental results on various datasets demonstrate the effectiveness and superiority of the proposed approach over existing methods.