Selective Visual Attention: Computational Models and Applications

4.2 Spectral Residual Approach

Regardless of the early spectral attentional model by using a DCT block mentioned in Itti et al.'s literature [2], the pure bottom-up model in the frequency domain was first proposed in [3] and called spectral residual, SR for short. This method needs only to compute the residual amplitude spectrum, and then the saliency map is just the recovered image by inverse FFT. There are no biological facts to confirm spectral processing in the brain, but almost the same saliency results can be obtained from the simple SR model compared to the BS model. In addition, its computational consumption is quite little. Remember that the BS model in C++ code named NVT or NVT+ [22] has high computation speed, but the SR model in the MATLAB® version is extremely parsimonious, so that it is absolutely suitable for real-time applications. Since the SR model was introduced, several improved or simpler computational models in the frequency domain have been proposed [5–10]. Putting aside the reason why the frequency model has good performance, we first introduce the idea of SR, its algorithm and some simulation results in this section. Some alternative frequency domain models will be illustrated in the following six sections. A biologically plausible model in the frequency domain is proposed in Section 4.6, which can partly interpret the rationality of these spectral models. It is worth noting that, up to now, all visual attention models in the frequency domain are pure bottom-up ...