ACM Home Page
Please provide us with feedback. Feedback
Multi-operator media retargeting
Full text PdfPdf (21.11 MB)
Source
ACM Transactions on Graphics (TOG) archive
Volume 28 ,  Issue 3  (August 2009) table of contents
Proceedings of ACM SIGGRAPH 2009
SESSION: Fast image processing and retargeting table of contents
Article No. 23  
Year of Publication: 2009
ISSN:0730-0301
Also published in ...
Authors
Michael Rubinstein  The Interdisciplinary Center, Herzliya
Ariel Shamir  The Interdisciplinary Center, Herzliya
Shai Avidan  Adobe Systems Inc.
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 73,   Downloads (12 Months): 224,   Citation Count: 0
Additional Information:

appendices and supplements   abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1531326.1531329
What is a DOI?

APPENDICES and SUPPLEMENTS
We supply an additional pdf document containing more results and comparison to the user results collected in our user study. We hope to publish further relevant material, including user data, on the project website: http://www.faculty.idc.ac.il/arik/SCWeb/multiop/index.html


ABSTRACT

Content aware resizing gained popularity lately and users can now choose from a battery of methods to retarget their media. However, no single retargeting operator performs well on all images and all target sizes. In a user study we conducted, we found that users prefer to combine seam carving with cropping and scaling to produce results they are satisfied with. This inspires us to propose an algorithm that combines different operators in an optimal manner. We define a resizing space as a conceptual multi-dimensional space combining several resizing operators, and show how a path in this space defines a sequence of operations to retarget media. We define a new image similarity measure, which we term Bi-Directional Warping (BDW), and use it with a dynamic programming algorithm to find an optimal path in the resizing space. In addition, we show a simple and intuitive user interface allowing users to explore the resizing space of various image sizes interactively. Using key-frames and interpolation we also extend our technique to retarget video, providing the flexibility to use the best combination of operators at different times in the sequence.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
Chen, B., and Sen, P. 2008. Video carving. In Short Papers Proceedings of Eurographics.
 
3
Chen, L., Xie, X., Fan, X., Ma, W., Zhang, H., and Zhou, H. 2003. A visual attention model for adapting images on small displays. Multimedia Systems 9, 4, 353--364.
 
4
Gal, R., Sorkine, O., and Cohen-Or, D. 2006. Feature-aware texturing. In Eurographics Symposium on Rendering, 297--303.
 
5
6
7
8
 
9
Sakoe, H. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26, 43--49.
 
10
11
12
 
13
Simakov, D., Caspi, Y., Shechtman, E., and Irani, M. 2008. Summarizing visual data using bidirectional similarity. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition.
14
 
15
16
17
 
18
Wolf, L., Guttmann, M., and Cohen-Or, D. 2007. Nonhomogeneous content-driven video-retargeting. In Proceedings of the Eleventh IEEE International Conference on Computer Vision (ICCV '07), 1--6.

Collaborative Colleagues:
Michael Rubinstein: colleagues
Ariel Shamir: colleagues
Shai Avidan: colleagues