Learning based thumbnail cropping

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 outperforms previous saliency based cropping.