“Optimized image resizing using seam carving and scaling”
Conference:
Type(s):
Title:
- Optimized image resizing using seam carving and scaling
Session/Category Title: Resizing/montage
Presenter(s)/Author(s):
Moderator(s):
Abstract:
We present a novel method for content-aware image resizing based on optimization of a well-defined image distance function, which preserves both the important regions and the global visual effect (the background or other decorative objects) of an image. The method operates by joint use of seam carving and image scaling. The principle behind our method is the use of a bidirectional similarity function of image Euclidean distance (IMED), while cooperating with a dominant color descriptor (DCD) similarity and seam energy variation. The function is suitable for the quantitative evaluation of the resizing result and the determination of the best seam carving number. Different from the previous simplex-mode approaches, our method takes the advantages of both discrete and continuous methods. The technique is useful in image resizing for both reduction/retargeting and enlarging. We also show that this approach can be extended to indirect image resizing.
References:
1. Avidan, S., and Shamir, A. 2007. Seam carving for content-aware image resizing. ACM Trans. Graph. 26, 3, 10. Google ScholarDigital Library
2. Chen, L., Xie, X., Fan, X., Ma, W., Zhang, H., and Zhou, H. 2003. A visual attention model for adapting images on small displays. ACM Multimedia Systems Journal 9, 4, 353–364.Google ScholarDigital Library
3. Cho, T. S., Butman, M., Avidan, S., and Freeman, W. T. 2008. The patch transform and its applications to image editing. In IEEE Conference on Computer Vision and Pattern Recognition 2008 (CVPR 2008)., 1–8.Google Scholar
4. DeCarlo, D., and Santella, A. 2002. Stylization and abstraction of photographs. ACM Trans. Graph. 21, 3, 769–776. Google ScholarDigital Library
5. El-Alfy, H., Jacobs, D., and Davis, L. 2007. Multi-scale video cropping. In MULTIMEDIA ’07: Proceedings of the 15th international conference on Multimedia, ACM, New York, NY, USA, 97–106. Google ScholarDigital Library
6. Gal, R., Sorkine, O., and Cohen-Or, D. 2006. Feature-aware texturing. In Proceedings of Eurographics Symposium on Rendering, 297–303. Google ScholarDigital Library
7. Itti, L., Koch, C., and Niebur, E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 11 (Nov), 1254–1259. Google ScholarDigital Library
8. Li, J., and Lu, B.-L. 2009. An adaptive image euclidean distance. Pattern Recogn. 42, 3, 349–357. Google ScholarDigital Library
9. Liu, H., Xie, X., Ma, W.-Y., and Zhang, H.-J. 2003. Automatic browsing of large pictures on mobile devices. In MULTIMEDIA ’03: Proceedings of the eleventh ACM international conference on Multimedia, ACM, New York, NY, USA, 148–155. Google ScholarDigital Library
10. Manjunath, B. S., Ohm, J. R., Vasudevan, V. V., and Yamada, A. 2001. Color and texture descriptors. Circuits and Systems for Video Technology, IEEE Transactions on 11, 6, 703–715. Google ScholarDigital Library
11. Manjunath, B., Salembier, P., and Sikora, T. 2002. Multimedia Content Description Interface. Wiley, Chichester. Google ScholarDigital Library
12. Min, R., and Cheng, H. D. 2009. Effective image retrieval using dominant color descriptor and fuzzy support vector machine. Pattern Recogn. 42, 1, 147–157. Google ScholarDigital Library
13. Pritch, Y., Kav-Venaki, E., and Peleg, S. 2009. Shift-map image editing. In ICCV 2009: Proceedings of the Twelfth IEEE International Conference on Computer Vision, 721.Google Scholar
14. Rubinstein, M., Shamir, A., and Avidan, S. 2008. Improved seam carving for video retargeting. ACM Trans. Graph. 27, 3, 16. Google ScholarDigital Library
15. Rubinstein, M., Shamir, A., and Avidan, S. 2009. Multioperator media retargeting. ACM Trans. Graph. 28, 3, 23. Google ScholarDigital Library
16. Santella, A., Agrawala, M., DeCarlo, D., Salesin, D., and Cohen, M. 2006. Gaze-based interaction for semiautomatic photo cropping. In CHI ’06: Proceedings of the SIGCHI conference on Human Factors in computing systems, ACM, New York, NY, USA, 771–780. Google ScholarDigital Library
17. Simakov, D., Caspi, Y., Shechtman, E., and Irani, M. 2008. Summarizing visual data using bidirectional similarity. In IEEE Conference on Computer Vision and Pattern Recognition 2008 (CVPR 2008), 1–8.Google ScholarCross Ref
18. Snyman, J. A. 2005. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Springer Publishing.Google Scholar
19. Suh, B., Ling, H., Bederson, B. B., and Jacobs, D. W. 2003. Automatic thumbnail cropping and its effectiveness. In UIST ’03: Proceedings of the 16th annual ACM symposium on User interface software and technology, ACM, New York, NY, USA, 95–104. Google ScholarDigital Library
20. Viola, P., and Jones, M. J. 2004. Robust real-time face detection. Int. J. Comput. Vision 57, 2, 137–154. Google ScholarDigital Library
21. Walthera, D., and Koch, C. 2006. Modeling attention to salient proto-objects. Neural Networks 19, 9, 1395–1407. Google ScholarDigital Library
22. Wang, L., Zhang, Y., and Feng, J. 2005. On the euclidean distance of images. IEEE Trans. Pattern Anal. Mach. Intell. 27, 8, 1334–1339. Google ScholarDigital Library
23. 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
24. Wei, L.-Y., Han, J., Zhou, K., Bao, H., Guo, B., and Shum, H.-Y. 2008. Inverse texture synthesis. ACM Trans. Graph. 27, 3, 52. Google ScholarDigital Library
25. Wolf, L., Guttmann, M., and Cohen-Or, D. 2007. Non-homogeneous content-driven video-retargeting. In Proceedings of the Eleventh IEEE International Conference on Computer Vision (ICCV-07) ICCV 2007, 1–6.Google Scholar
26. Zhang, Y. F., Hu, S. M., and Martin, R. R. 2008. Shrinkability maps for content-aware video resizing. Computer Graphics Forum 27, 7, 1797–1804.Google ScholarCross Ref
