We introduce the notion of co-saliency for image matching. Our matching algorithm combines the discriminative power of feature correspondences with the descriptive power of matching segments. Co-saliency matching score favors correspondences that are consistent with ’soft’ image segmentation as well as with local point feature matching. We express the matching model via a joint image graph (JIG) whose edge weights represent intra- as well as inter-image relations. The dominant spectral components of this graph lead to simultaneous pixel-wise alignment of the images and saliency-based synchronization of ’soft’ image segmentation. The co-saliency score function, which characterizes these spectral components, can be directly used as a similarity metric as well as a positive feedback for updating and establishing new point correspondences. We present experiments showing the extraction of matching regions and pointwise correspondences, and the utility of the global image similarity in the context of place recognition.