This paper presents an efficient method for finding salient differential features in images. We argue that the problem of finding salient features among all the possible ones is equivalent to finding outliers in a high-dimensional data set. We apply outlier detection techniques used in data mining to devise a linear time algorithm to extract the salient features. This yields a definition of saliency which rests on a more principled basis and also produces more reliable feature correspondences between images than the more conventional ones.