Saliency-Driven Variational Retargeting for Historical Maps

We study the problem of georeferencing artistic historical maps. Since they were primarily conceived as work of art more than an accurate cartographic tool, the common warping approaches implemented in Geographic Application Systems (GIS) usually lead to an overly-stretched image in which the actual pictorial content (like written text, compass roses, buildings, etc.) is un-naturally deformed. On the other hand, domain transformation of images driven by the perceived salient visual content is a well-known topic known as “image retargeting” which has been mostly limited to a change of scale of the image (i.e. changing the width and height) rather than a more general control-points based warping. In this work we propose a variational image retargeting approach in which the local transformations are estimated to accommodate a set of control points instead of image boundaries. The direction and severity of warping is modulated by a novel tensor-based saliency formulation considering both the visual content and the shape of the underlying features to transform. The optimization includes a flow projection step based on the isotonic regression to avoid singularities and flip overs of the resulting distortion map.