1. A computer-implemented method, comprising: identifying sample regions that each include a plurality of adjacent pixels of an image; calculating, for pixels in the sample regions and by one or more processors, a foreground saliency value of the pixel based, at least in part, on a conditional probability that the pixel is a foreground pixel based on a foreground color model and a conditional probability that the pixel is a background pixel based on a background color model; using a classifier to calculate a respective combined saliency value for each pixel based on the foreground saliency value for the pixel and a saliency value for at least one different saliency cue function wherein the classifier is trained on training image and corresponding saliency values and wherein the respective combined saliency value for each of the training images comprise saliency values for a plurality of respective pixels in the training image; and determining, by one or more processors, a salient region of the pixels with a subwindow search based on the respective combined saliency values in a rectangle that maximizes a sum of combined saliency values of pixels in the rectangle and a complement of the combined saliency values of the pixels in the rectangle.