Image retargeting based on the sensitivity-tuned visual significance map

In this paper, we propose a novel image retargeting algorithm based on the sensitivity-tuned visual significance map which is composed of a saliency map and a gradient map. We develop a new saliency detection model based on the human visual sensitivity and amplitude spectrum of image patches. We use a coherent normalization based fusion method to combine the saliency map and the gradient map to generate the visual significance map. The seam carving technique is adopted for image retargeting, based on the sensitivity-tuned visual significance map. Experiment results show that the proposed algorithm outperforms the relevant state-of-the-arts image retargeting algorithms significantly.