Selective Visual Attention: Computational Models and Applications

4.6 Divisive Normalization Model in the Frequency Domain

While there has not been any biological evidence to suggest that cortical neurons are capable of performing a Fourier transform, there has been much evidence suggesting that they are capable of decomposing the visual stimulus into components, which are localized in both space and frequency domain, by using linear filters [56]. Thereby most spatial domain computational models – such as the BS model and its variations, and the AIM model – use linear filters (ICA basis functions or Gabor filters) to approximate the simple cortical response. In the filtering stage, the input image is decomposed into many feature maps. Then, lateral inhibition (centre–surround) – with its well known properties exhibited in simple cells of the V1 – is implemented in these feature maps to emphasize areas with high saliency. Finally, in the spatial domain, all feature maps are combined into a saliency map. Since features extraction in these spatial domain models needs a larger bank of filters, each image channel needs centre–surround processing which results in computational complexity. Contrarily, frequency domain models have very fast computation speed compared to spatial domain models, but in return, there isn't enough biological evidence to explain it. An idea has been proposed in [8, 45] to link the spatial model and the frequency domain model; it derives the frequency domain equivalent to the spatial domain models of biological processes. The ...