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In this article, we propose a bottom-up saliency model which works on capturing the contrast between random pixels in an image. The model is explained on the basis of the stimulus bias between two given stimuli (pixel intensity values) in an image and has a minimal set of tunable parameters. The methodology does not require any training bases or priors. We followed an established experimental setting and obtained state-of-the-art-results for salient region detection on the MSR dataset. Further experiments demonstrate that our method is robust to noise and has, in comparison to six other state-of-the-art models, a consistent performance in terms of recall, precision and F-measure.