Scale and object aware image retargeting for thumbnail browsing

  • Authors:

  • Jin Sun;Haibin Ling

  • Affiliations:

  • Center for Data Analytics & Biomedical Informatics, Dept. of Computer & Information Sciences, Temple University, Philadelphia, 19122, USA;Center for Data Analytics & Biomedical Informatics, Dept. of Computer & Information Sciences, Temple University, Philadelphia, 19122, USA

  • Venue:
  • ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
  • Year:
  • 2011

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Abstract

Many image retargeting algorithms, despite aesthetically carving images smaller, pay limited attention to image browsing tasks where tiny thumbnails are presented. When applying traditional retargeting methods for generating thumbnails, several important issues frequently arise, including thumbnail scales, object completeness and local structure smoothness. To address these issues, we propose a novel image retargeting algorithm, Scale and Object Aware Retargeting (SOAR), which has four components: (1) a scale dependent saliency map to integrate size information of thumbnails, (2) objectness (Alexe et al. 2010) for preserving object completeness, (3) a cyclic seam carving algorithm to guide continuous retarget warping, and (4) a thin-plate-spline (TPS) retarget warping algorithm that champions local structure smoothness. The effectiveness of the proposed algorithm is evaluated both quantitatively and qualitatively. The quantitative evaluation is conducted through an image browsing user study to measure the effectiveness of different thumbnail generating algorithms, followed by the ANOVA analysis. The qualitative study is performed on the RetargetMe benchmark dataset. In both studies, SOAR generates very promising performance, in comparison with state-of-the-art retargeting algorithms.