Abstract
Content-aware image retargeting, also known as seam carving or content aware scaling, is an algorithm for image resizing developed in recent years. In this paper, we present a content-aware image retargeting method by combining the line-based Moving Least Squares (MLS) deformation and saliency map. We develop a salience-related weight of MLS deformation that allows manually defining regions in which pixels may not be moved. Therefore, the new image retargeting can change non-vital parts of the image while preserving local shapes in the areas with high saliency. Benefited from the line-based MLS deformation, our method can retarget image from rectangle to arbitrary polygon. When the constrained lines are at the interior of the boundary of image, our method can also deal with the artistic perspective manipulation problem well. The purpose of developing this algorithm is to display images without distortion on various media in the field of industrial, entertainment or artistic creation. Experimental results show that the proposed method outperforms existing methods in terms of visual performance.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Y.S., Tai, C.L., Sorkine, O., Lee, T.Y.: Optimized scale-and-stretch for image resizing. ACM Trans. Graph. 27(5), 118:1–118:8 (2008)
Silveira, C.L.B., de Oliveira Mierlo, F., Luiz Gonzaga, J., da Costa, C.A., Farias, K., da Rosa Righi, R.: Faster seam carving with minimum energy windows. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 45–48 (2014)
Kiess, J., Gritzner, D., Guthier, B., Kopf, S., Effelsberg, W.: GPU video retargeting with parallelized seamcrop. In: Proceedings of the 5th ACM Multimedia Systems Conference, pp. 139–147 (2014)
Schaefer, S., McPhail, T., Warren, J.: Image deformation using moving least squares. ACM Trans. Graph. 25(3), 533–540 (2006). Special issue of ACM SIGGRAPH 2006
Ma, J., Zhao, J., Tian, J.: Nonrigid image deformation using moving regularized least squares. IEEE Signal Process. Lett. 20(10), 988–991 (2013)
Zhang, Y., Lai, J., Du, X.: Vision-related MLS image deformation using saliency map. In: 6th International Conference on Image and Graphics (ICIG), pp. 193–198, August 2011
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of Neural Information Processing Systems (NIPS), pp. 545–552 (2006)
Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 473–480, June 2011
Krahenbuhl, P., Lang, M., Hornung, A., Gross, M.: A system for retargeting of streaming video. ACM Transactions on Graphics: Proceedings of ACM SIGGRAPH Asia 28(5) (2009)
Carroll, R., Agarwala, A., Agrawala, M.: Image warps for artistic perspective manipulation. ACM Trans. Graph. 29(4), 127:1–127:9 (2010). Special Issue of ACM SIGGRAPH 2010
Mitra, N.J., Wand, M., Zhang, H., Cohen-Or, D., Kim, V., Huang, Q.X.: Structure-aware shape processing. In: ACM SIGGRAPH Courses (2014)
Gal, R., Sorkine, O., Cohen-Or, D.: Feature-aware texturing. In: Proceedings of Eurographics Symposium on Rendering, pp. 297–303 (2006)
Shamir, A., Avidan, S.: Seam carving for media retargeting. Commun. ACM 52(1), 77–85 (2009)
Acknowledgements
This work was supported by The Provincial Production Education and Research Major Projects under Grant No. 2009A 090100025.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer-Verlag GmbH Germany
About this chapter
Cite this chapter
Li, X., Zhang, Y., Du, X. (2017). Content-Aware Image Retargeting Using Line-Based MLS Deformation. In: Pan, Z., Cheok, A., Müller, W., Zhang, M. (eds) Transactions on Edutainment XIII. Lecture Notes in Computer Science(), vol 10092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54395-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-662-54395-5_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-54394-8
Online ISBN: 978-3-662-54395-5
eBook Packages: Computer ScienceComputer Science (R0)