Fast Bottom‐Up Computational Models in the Spectral Domain
Liming Zhang, Weisi Lin · 2013
This chapter continues the introduction to bottom-up visual attention models. Following the description of models in the spatial (pixel) domain in the previous chapter, the focus is now put on models in the spectral domain. Since frequency domain models can detect the salient object quicker to enable them to meet real-time requirements in engineering, they are the choice for many real-world applications In this chapter, first the properties of the frequency spectrum for image analysis are given in Section 4.1, and then the major bottom-up computational models based on phase spectrum in frequency domain are presented in Sections 4.2–4.6: the SR, PFT, PQFT, PCT and FDN models, respectively. In Section 4.6, FDN and PFDN models have biological plausibility because they simulate each step from the (spatial domain) BS model, but in the frequency domain. In Section 4.7, the AQFT model based on amplitude spectrum of image patches is introduced and Section 4.8 gives a computational model from the JPEG bit-stream. Finally, the advantages and limitations of frequency computational models are discussed in Section 4.9.