Content-aware image resizing based on random-carving with probability

To improve the running speed of image resizing, a fast content-aware image resizing algorithm was proposed based on the threshold learning and random-carving with probability. Firstly the important map was calculated by combining the graph-based visual saliency map and gradient map. Then the image threshold value was obtained by radial basis function (RBF) neural network learning. And by the threshold, the original image was separated into the protected part and the unprotected part which was corresponding to the important part and the unimportant part of the original image individually. Finally, the two parts were allocated different resizing scales and the random-carving with probability was applied to them respectively. Experiments results show that the proposed algorithm has lower time cost comparing to the state-of-arts algorithms in MSRA image database, and has a better visual perception on image resizing.