In this paper we propose a novel approach to find a global correspondence between two images by maximizing mutual information in the presence of large scale changes and rotations. Our approach combines the local descriptors with global search. We have tested our method on various test images and compared the matching performance with the SIFT descriptors. The experimental results show that the proposed local descriptor and graph-based search provides robust point matching for scale, rotation and illumination changes.