基于概率随机裁剪的图像缩放算法

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

为提高图像缩放的速度,提出一种结合阈值学习与依概率随机裁剪的快速内容感知图像缩放算法,通过计算图像的重要度图,利用径向基函数(RBF,radial basis function)神经网络进行阈值学习求出图像的重要度阈值,根据阈值将图像分成保护区域和非保护区域,并按缩放要求为其分配不同的缩放比,分别进行依概率随机裁剪。在MSRA图像数据库上与目前流行的内容感知缩放方法进行对比,实验结果表明,所提方法的缩放时间明显低于其他算法,而且在缩放效果上有明显的优势。

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.