This paper addresses the problem of detecting salient objects in images, formulating it as an image segmentation task. The authors propose a set of novel features, including multi-scale contrast, center-surround histogram, and color spatial distribution, to describe salient objects at local, regional, and global levels. These features are combined using a Conditional Random Field (CRF) model to improve detection accuracy. A large image database with over 20,000 well-labeled images is constructed for training and evaluation. The CRF model is trained to learn the optimal combination of these features. The effectiveness of the proposed approach is demonstrated through experiments on two image sets, showing significant improvements over existing methods in terms of precision and boundary displacement error. The paper also discusses the limitations and future directions for salient object detection, including handling non-rectangular shapes and multiple salient objects.This paper addresses the problem of detecting salient objects in images, formulating it as an image segmentation task. The authors propose a set of novel features, including multi-scale contrast, center-surround histogram, and color spatial distribution, to describe salient objects at local, regional, and global levels. These features are combined using a Conditional Random Field (CRF) model to improve detection accuracy. A large image database with over 20,000 well-labeled images is constructed for training and evaluation. The CRF model is trained to learn the optimal combination of these features. The effectiveness of the proposed approach is demonstrated through experiments on two image sets, showing significant improvements over existing methods in terms of precision and boundary displacement error. The paper also discusses the limitations and future directions for salient object detection, including handling non-rectangular shapes and multiple salient objects.