“Resizing by symmetry-summarization” – ACM SIGGRAPH HISTORY ARCHIVES

“Resizing by symmetry-summarization”

  • 2010 SA Technical Paper: Wu_Resizing by symmetry-summarization

Conference:


Type(s):


Title:

    Resizing by symmetry-summarization

Session/Category Title:   Image & video applications


Presenter(s)/Author(s):


Moderator(s):



Abstract:


    Image resizing can be achieved more effectively if we have a better understanding of the image semantics. In this paper, we analyze the translational symmetry, which exists in many real-world images. By detecting the symmetric lattice in an image, we can summarize, instead of only distorting or cropping, the image content. This opens a new space for image resizing that allows us to manipulate, not only image pixels, but also the semantic cells in the lattice. As a general image contains both symmetry & non-symmetry regions and their natures are different, we propose to resize symmetry regions by summarization and non-symmetry region by warping. The difference in resizing strategy induces discontinuity at their shared boundary. We demonstrate how to reduce the artifact. To achieve practical resizing applications for general images, we developed a fast symmetry detection method that can detect multiple disjoint symmetry regions, even when the lattices are curved and perspectively viewed. Comparisons to state-of-the-art resizing techniques and a user study were conducted to validate the proposed method. Convincing visual results are shown to demonstrate its effectiveness.

References:


    1. Ahuja, N., and Todorovic, S. 2007. Extracting texels in 2.1d natural textures. In ICCV, 1–8.Google Scholar
    2. Avidan, S., and Shamir, A., 2007. Seam carving for content-aware image resizing. ACM Trans. Graph. 26, 3, 10. Google ScholarDigital Library
    3. Barnes, C., Shechtman, E., Finkelstein, A., and Goldman, D. B. 2009. PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Trans. Graph 28, 3. Google ScholarDigital Library
    4. Canny, F. J. 1986. A Computational Approach to Edge Detection. IEEE Trans. PAMI 8, 6, 679–698. Google ScholarDigital Library
    5. Chen, L. Q., Xie, X., Fan. X., Ma, W. Y., Zhang, H. J. and Zhou, H., Q. 2003. A visual attention model for adapting images on small displays. ACM Multimedia Systems Journal 9, 4, 353–364.Google ScholarDigital Library
    6. Cheng, M.-M., Zhang, F.-L., Mitra, N. J., Huang, X., and Hu, S.-M. 2010. Repfinder: Finding approximately repeated scene elements for image editing. ACM Trans. Graph 29, 3. Google ScholarDigital Library
    7. Cho, T. S., Butman, M., Avidan, S., and Freeman, W. T., 2008. The patch transform and its applications to image editing. In CVPR ’08.Google Scholar
    8. Comaniciu, D., and Meer, P. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal. Mach. Intell. 24, 5, 603–619. Google ScholarDigital Library
    9. Dong, W., Zhou, N., Paul, J.-C., and Zhang, X. 2009. Optimized image resizing using seam carving and scaling. ACM Trans. Graph. 28, 5, 1–10. Google ScholarDigital Library
    10. Gal, R., Sorkine, O., and Cohen-Or, D. 2006. Feature-aware texturing. In EGSR ’06, 297–303. Google ScholarDigital Library
    11. Grünbaum, B., and Shephard, G. C. 1986. Tilings and patterns. W. H. Freeman & Co., New York, NY, USA. Google ScholarDigital Library
    12. Harris, C., and Stephens, M. 1988. A combined corner and edge detector. In Proceedings of the 4th Alvey Vision Conference, 147–151.Google Scholar
    13. Hays, J., Leordeanu, M., Efros, A. A., and Liu, Y. 2006. Discovering texture regularity as a higher-order correspondence problem In ECCV (2), 522–535. Google ScholarDigital Library
    14. Kwatra, V., Schödl, A., Essa, I. A., Turk, G., and Bobick., A. F. 2003 Graphcut textures: Image and video synthesis using graph cuts. ACM Transactions on Graphics, SIGGRAPH 2003 22, 3 (July), 277–286. Google ScholarDigital Library
    15. Leung, T. K., and Malik, J. 1996. Detecting, localizing and grouping repeated scene elements from an image. In ECCV (1), 546–555. Google ScholarDigital Library
    16. Lin, W.-C., and Liu, Y., 2007. A lattice-based mrf model for dynamic near-regular texture tracking. IEEE Trans Pattern Anal. Mach. Intell. 29, 5, 777–792. Google ScholarDigital Library
    17. Liu, H., Xie, X., Ma, W.-Y., and Zhang, H.-J. 2003. Automatic browsing of large pictures on mobile devices. In Proceedings of ACM International Conference on Multimedia, 148–155. Google ScholarDigital Library
    18. Liu, Y., Collins, R. T., and Tsin, Y. 2004. A computational model for periodic pattern perception based on frieze and wallpaper groups. IEEE Trans. Pattern Anal. Mach. Intell. 26, 3, 354–371. Google ScholarDigital Library
    19. Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 2, 91–110. Google ScholarDigital Library
    20. Matas, J., Chum, O., Urban, M., and Pajdla, T. 2002. Robust wide baseline stereo from maximally stable extremal regions. In In British Machine Vision Conference, 384–393.Google Scholar
    21. Park, M., Brocklehurst, K., Collins, R. T., and Liu, Y. 2009. Deformed lattice detection in real-world images using mean-shift belief propagation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 10, 1804–1816. Google ScholarDigital Library
    22. Pritch, Y., Kav-Venaki, E., and Peleg, S. 2009. Shift-map image editing. In ICCV’09.Google Scholar
    23. Rubinstein, M., Shamir, A., and Avidan, S. 2008. Improved seam carving for video retargeting. ACM Trans. Graph. 27, 3, 16. Google ScholarDigital Library
    24. Rubinstein, M., Shamir, A., and Avidan, S. 2009. Multi-operator media retargeting. ACM Trans. Graph. 28, 3, 23. Google ScholarDigital Library
    25. Santella, A., Agrawala, M., DeCarlo, D., Salesin, D., and Cohen, M. 2006. Gaze-based interaction for semi-automatic photo cropping. In Proceedings of CHI, 771–780. Google ScholarDigital Library
    26. Schattschneider, D. 1978. The plane symmetry groups: Their recognition and notation. The American Mathematical Monthly 85, 6, 439–450.Google ScholarCross Ref
    27. Shi, J., and Tomasi, C., 1994. Good features to track. IEEE Conference on Computer Vision and Pattern Recognition, 593–600.Google Scholar
    28. Simakov, D., Caspi, Y., Shechtman, E., and Irani, M., 2008. Summarizing visual data using bidirectional similarity. In CVPR ’08.Google Scholar
    29. Suh, B., Ling, H., Bederson, B. B., and Jacobs, D. W. 2003. Automatic thumbnail cropping and its effectiveness. In Proceedings of UIST, 95–104. Google ScholarDigital Library
    30. Wang, Y.-S., Tai, C.-L., Sorkine, O., and Lee, T.-Y. 2008. Optimized scale-and-stretch for image resizing. ACM Trans. Graph. 27, 5, 118. Google ScholarDigital Library
    31. Wang, Y.-S., Fu, H., Sorkine, O., Lee, T.-Y., and Seidel, H.-P. 2009. Motion-aware temporal coherence for video resizing. ACM Trans. Graph. 28, 5. Google ScholarDigital Library
    32. Wang, Y.-S., Lin, H.-C., Sorkine, O., and Lee, T.-Y. 2010. Motion-based video retargeting with optimized crop-and-warp. ACM Trans. Graph. (Proceedings of ACM SIGGRAPH) 29, 3. Google ScholarDigital Library
    33. Wolf, L., Guttmann, M., and Cohen-Or, D., 2007. Non-homogeneous content-driven video-retargeting. In ICCV ’07.Google Scholar
    34. Zhang, Y.-F., Hu, S.-M., and Martin, R. R. 2008. Shrinkability maps for content-aware video resizing. In PG ’08.Google Scholar
    35. Zhang, G.-X., Cheng, M.-M., Hu, S.-M., and Martin, R. R., 2009. A shape-preserving approach to image resizing. Computer Graphics Forum 28, 7, 1897–1906.Google ScholarCross Ref


ACM Digital Library Publication:



Overview Page:



Submit a story:

If you would like to submit a story about this presentation, please contact us: historyarchives@siggraph.org