This paper presents a supervised approach for salient object detection, formulated as an image segmentation problem using a set of local, regional, and global features. The authors propose a Conditional Random Field (CRF) to combine these features for salient object detection. They also construct a large image database with tens of thousands of carefully labeled images for quantitative evaluation of visual attention algorithms. The database is publicly available. The paper introduces three types of features: multi-scale contrast, center-surround histogram, and color spatial distribution. Multi-scale contrast captures high contrast boundaries, center-surround histogram measures the difference between a salient object and its surrounding context, and color spatial distribution describes the global spatial distribution of colors in an image. These features are combined using CRF learning to detect salient objects. The authors evaluate their approach on their image database and compare it with existing methods. They find that their approach achieves higher precision and lower boundary displacement error compared to other methods. The method is particularly effective for small salient objects, where high precision is crucial. The paper also discusses the challenges of salient object detection, including hierarchical detection and the need for more sophisticated visual features. The authors conclude that their approach provides a robust and effective method for salient object detection, with potential applications in content-based image retrieval and automatic image collection. Future work includes extending the method to detect multiple salient objects and exploring non-rectangular shapes and non-linear feature combinations.This paper presents a supervised approach for salient object detection, formulated as an image segmentation problem using a set of local, regional, and global features. The authors propose a Conditional Random Field (CRF) to combine these features for salient object detection. They also construct a large image database with tens of thousands of carefully labeled images for quantitative evaluation of visual attention algorithms. The database is publicly available. The paper introduces three types of features: multi-scale contrast, center-surround histogram, and color spatial distribution. Multi-scale contrast captures high contrast boundaries, center-surround histogram measures the difference between a salient object and its surrounding context, and color spatial distribution describes the global spatial distribution of colors in an image. These features are combined using CRF learning to detect salient objects. The authors evaluate their approach on their image database and compare it with existing methods. They find that their approach achieves higher precision and lower boundary displacement error compared to other methods. The method is particularly effective for small salient objects, where high precision is crucial. The paper also discusses the challenges of salient object detection, including hierarchical detection and the need for more sophisticated visual features. The authors conclude that their approach provides a robust and effective method for salient object detection, with potential applications in content-based image retrieval and automatic image collection. Future work includes extending the method to detect multiple salient objects and exploring non-rectangular shapes and non-linear feature combinations.