The nature of statistical learning theory
The nature of statistical learning theory
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
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Scale and Object Aware Image Thumbnailing
International Journal of Computer Vision
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Thumbnail cropping helps improve thumbnail readability by cropping images before shrinking them. In this paper we propose a learning based method for automatic thumbnail cropping. To this end, we use a support vector machine to learn a discriminative model that simultaneously captures the saliency distribution and spatial priors. The model is then used to determine the best cropping rectangle. The proposed approach improves traditional saliency based cropping techniques by introducing the spatial priors, which is automatically learned through learning process. The new method is tested on images from the PASCAL08 dataset, where it out-performs previous saliency based cropping.