Fast image retargeting via axis-aligned importance scaling

Sunghwan Choi, Bumsub Ham, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we propose an image retargeting method that resizes an image by the axis-aligned importance scaling. The proposed method operates on the axis-aligned deformation space, where the mesh structure is parameterized in the 1D vector, i.e., quads on the same column (or row) share the single parameter. The unknown variables are thus dependent on the grid resolution only, allowing a fast and simple implementation on a moderate CPU. The optimal parameters are inferred in an iterative manner: these parameters are updated by scaling initial ones according to the deformation error. It is measured at each iteration by aggregating the transition cost for the deformed quad. Experimental results show that the proposed method preserves visually salient features without foldover artifacts better than competing methods. In addition, the optimal parameters can be calculated within 0.02 ms on a single-core CPU.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PublisherIEEE Computer Society
Pages1061-1065
Number of pages5
ISBN (Print)9781479923410
DOIs
Publication statusPublished - 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

Bibliographical note

Funding Information:
This work has been partially supported by the project CICYT-Project No. TIC 94-0561 and by CIRIT Project Ref. 1995SGR 00192.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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