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.
KeywordsImage retargeting Warping Historical maps
- 1.Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org
- 2.Achanta, R., Süsstrunk, S.: Saliency detection for content-aware image resizing. In: Proceedings of the 16th IEEE International Conference on Image Processing, ICIP 2009, pp. 1001–1004. IEEE Press, Piscataway (2009)Google Scholar
- 5.Gal, R., Sorkine, O., Cohen-Or, D.: Feature-aware texturing. In: Proceedings of the 17th Eurographics Conference on Rendering Techniques, EGSR 2006, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, pp. 297–303 (2006). https://doi.org/10.2312/EGWR/EGSR06/297-303
- 6.Guo, Y., Liu, F., Shi, J., Zhou, Z.H., Gleicher, M.: Image retargeting using mesh parametrization. IEEE Trans. Multimedia 11(5), 856–867 (2009). http://dblp.uni-trier.de/db/journals/tmm/tmm11.html#GuoLSZG09CrossRefGoogle Scholar
- 7.Lin, S.S., Yeh, I.C., Lin, C.H., Lee, T.Y.: Patch-based image warping for content-aware retargeting. IEEE Trans. Multimedia 15(2), 359–368 (2013). http://dblp.uni-trier.de/db/journals/tmm/tmm15.html#LinYLL13CrossRefGoogle Scholar
- 8.Pritch, Y., Kav-Venaki, E., Peleg, S.: Shift-map image editing. In: ICCV, pp. 151–158. IEEE Computer Society (2009). http://dblp.uni-trier.de/db/conf/iccv/iccv2009.html#PritchKP09
- 9.Rubinstein, M., Shamir, A., Avidan, S.: Multi-operator media retargeting. ACM Trans. Graph. 28(3) (2009). http://dblp.uni-trier.de/db/journals/tog/tog28.html#RubinsteinSA09CrossRefGoogle Scholar
- 10.Shamir, A., Sorkine, O.: Visual media retargeting. In: ACM SIGGRAPH ASIA 2009 Courses, SIGGRAPH ASIA 2009, pp. 11:1–11:13. ACM, New York (2009). https://doi.org/10.1145/1665817.1665828
- 11.Wang, Y.S., Tai, C.L., Sorkine, O., Lee, T.Y.: Optimized scale-and-stretch for image resizing. ACM Trans. Graph. 27(5), 118 (2008). http://dblp.uni-trier.de/db/journals/tog/tog27.html#WangTSL08CrossRefGoogle Scholar
- 12.Wolf, L., Guttmann, M., Cohen-Or, D.: Non-homogeneous content-driven video-retargeting. In: ICCV, pp. 1–6. IEEE Computer Society (2007). http://dblp.uni-trier.de/db/conf/iccv/iccv2007.html#WolfGC07