  We address the issue of visual saliency from three perspectives. First, we
consider saliency detection as a frequency domain analysis problem. Second, we
achieve this by employing the concept of {\it non-saliency}. Third, we
simultaneously consider the detection of salient regions of different size. The
paper proposes a new bottom-up paradigm for detecting visual saliency,
characterized by a scale-space analysis of the amplitude spectrum of natural
images. We show that the convolution of the {\it image amplitude spectrum} with
a low-pass Gaussian kernel of an appropriate scale is equivalent to such an
image saliency detector. The saliency map is obtained by reconstructing the 2-D
signal using the original phase and the amplitude spectrum, filtered at a scale
selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform
performs the analysis in the frequency domain. Using available databases, we
demonstrate experimentally that the proposed model can predict human fixation
data. We also introduce a new image database and use it to show that the
saliency detector can highlight both small and large salient regions, as well
as inhibit repeated distractors in cluttered images. In addition, we show that
it is able to predict salient regions on which people focus their attention.
