We present a multi-scale approach that uses Laplacian eigenvectors to extract globally significant contours from an image. The input images are mapped into the Laplacian space by using Laplacian eigenvectors. This mapping causes globally significant pixels along the contours to expand in the Laplacian space. The measure of the expansion is used to compute the Global Contours. We apply our scheme to real color images and compare it with several other methods that compute image and color saliency. The contours calculated by our method reflect global properties of the image and are complementary to classic center-surround image saliency methods. We believe that hybrid image saliency algorithms that combine our method of Global Contours with center-surround image saliency algorithms will be able to better characterize the most important regions of images than those from just using contours calculated using bottom-up approaches.