Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic thumbnail cropping and its effectiveness
Proceedings of the 16th annual ACM symposium on User interface software and technology
Summary thumbnails: readable overviews for small screen web browsers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic image retargeting with fisheye-view warping
Proceedings of the 18th annual ACM symposium on User interface software and technology
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Proceedings of the 15th international conference on Multimedia
Improved seam carving for video retargeting
ACM SIGGRAPH 2008 papers
Optimized scale-and-stretch for image resizing
ACM SIGGRAPH Asia 2008 papers
Perceptual Scale-Space and Its Applications
International Journal of Computer Vision
Multi-operator media retargeting
ACM SIGGRAPH 2009 papers
A system for retargeting of streaming video
ACM SIGGRAPH Asia 2009 papers
Image retargeting using mesh parametrization
IEEE Transactions on Multimedia
Learning based thumbnail cropping
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Energy-based image deformation
SGP '09 Proceedings of the Symposium on Geometry Processing
Motion-based video retargeting with optimized crop-and-warp
ACM SIGGRAPH 2010 papers
Resizing by symmetry-summarization
ACM SIGGRAPH Asia 2010 papers
A comparative study of image retargeting
ACM SIGGRAPH Asia 2010 papers
Scene carving: scene consistent image retargeting
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Saliency density maximization for object detection and localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Learning crop regions for content-aware generation of thumbnail images
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Image resizing via non-homogeneous warping
Multimedia Tools and Applications
Importance filtering for image retargeting
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
The effects of a visual fidelity criterion of the encoding of images
IEEE Transactions on Information Theory
Scale and object aware image retargeting for thumbnail browsing
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Measuring the Objectness of Image Windows
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we study effective approaches to create thumbnails from input images. Since a thumbnail will eventually be presented to and perceived by a human visual system, a thumbnailing algorithm should consider several important issues in the process including thumbnail scale, object completeness and local structure smoothness. To address these issues, we propose a new thumbnailing framework named scale and object aware thumbnailing (SOAT), which contains two components focusing respectively on saliency measure and thumbnail warping/cropping. The first component, named scale and object aware saliency (SOAS), models the human perception of thumbnails using visual acuity theory, which takes thumbnail scale into consideration. In addition, the "objectness" measurement (Alexe et al. 2012) is integrated in SOAS, as to preserve object completeness. The second component uses SOAS to guide the thumbnailing based on either retargeting or cropping. The retargeting version uses the thin-plate-spline (TPS) warping for preserving structure smoothness. An extended seam carving algorithm is developed to sample control points used for TPS model estimation. The cropping version searches a cropping window that balances the spatial efficiency and SOAS-based content preservation. The proposed algorithms were evaluated in three experiments: a quantitative user study to evaluate thumbnail browsing efficiency, a quantitative user study for subject preference, and a qualitative study on the RetargetMe dataset. In all studies, SOAT demonstrated promising performances in comparison with state-of-the-art algorithms.