Detecting salient contours in complex backgrounds is important in image analysis and scene understanding. The local context of an edge or line segment feature is commonly used to measure its saliency degree as a part of the object boundary. However, traditionally the context information is captured by studying several features in the predefined neighborhood. In this paper, a novel salient contour extraction algorithm based on the multi-resolution analysis is proposed and a new saliency measure is defined to characterize the significance of feature. Relation of features that are corresponding to the same part of the object boundary across resolutions is utilized to estimate the context information and feature significance value. Experimental results show that the proposed method can extract salient contours more efficiently than center-surround interaction based methods and still provide robust results.