Abstract
The image resizing methods are roughly categoriz-ed into two classes: simple scaling method and content aware resizing. The former is trivial, while the later is able to preserve visually prominent features in the resized image. The measure approach of visual saliency plays an important role in the content aware resizing method. This paper introduces a new saliency measure, self-adapting significant map, which characterizes the prominent features using gradient, saliency map and resizing ratio. Owing to taking resizing ratio into consideration, our computation approach automatically smoothes the significant map when the resizing is highly non-homogenous. Based on this computation approach, we propose a new content-based resizing method, which can reasonably preserve the important and less-important regions according to the resizing ratio. Experimental results show that our method outperforms the similar resizing method when the resizing ratio is bigger.
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References
Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graphics 26(3) (2007)
Rubinsrein, M., Shamir, A., Avdian, S.: Improved seam carving for video retargeting. ACM Trans. Graphics 27(3) (2008)
Wolf, L., Guttmann, M., Cohen-or, D.: Nonhomogeneous content-driven video-retargeting. In: Proceedings of IEEE ICCV (2007)
Wang, Y.-S., Tai, C.-L., Sorkine, O., Lee, T.-Y.: Optimized scale-and-stretch for image resizing. ACM Trans on Graphics 27(5), 1–8 (2008)
Guo, Y., Liu, F., Shi, J., Zhou, Z., Gleicher, M.: Image retargeting using mesh parameterization. IEEE Trans. on Multimedia 11(5), 856–867 (2009)
Zhang, G., Cheng, M., Hu, S., Martin, R.: A shape-preserving approach to image resizing. Pacific Graphics 28(7), 1897–1906 (2009)
Itti, L., Koth, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Chen, L.-Q., Xie, X., Fan, X., Ma, W.-Y., Zhang, H.-J., Zhou, H.-Q.: A visual attention model for adapting images on small displays. Multimedia Systems 9(4), 353–364 (2003)
Kraevoy, V., Sheffer, A., Shamir, A., Cohen-or, D.: Non-homogeneous resizing of complex models. ACM Trans. on Graphics 27(5), 1–9 (2008)
Shi, J., Guo, Y.-W., Du, Z.-L., Zhang, F.-Y., Peng, Q.-S.: A mesh parameterization-based image retargeting method. Journal of Software 19, 19–30 (2008)
Igarashi, T., Moscovich, T., Hughes, J.-F.: As-rigid-as-possible shape manipulation. ACM Trans. Graphics 24(3) (2005)
Fang, H., Hart, J.-C.: Detail preserving shape deformation in image editing. ACM Trans. Graphics 26(3), 12 (2007)
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© 2011 Springer-Verlag Berlin Heidelberg
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Fenghui, N., Haisheng, L. (2011). Image Resizing Based on Self-adapting Significant Map. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23214-5_86
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DOI: https://doi.org/10.1007/978-3-642-23214-5_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23213-8
Online ISBN: 978-3-642-23214-5
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