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Selective Visual Attention: Computational Models and Applications by Weisi Lin, Liming Zhang

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4.9 Further Discussions of Frequency Domain Approach

This chapter introduces several visual attention models in frequency domain: SR, PFT, PQFT, pulsed PCA, PCT, FDN, PFDN and AQFT. Modelling directly from a bit-stream of compressed images is also discussed. These models complete the computation of the saliency map with the help of FFT and DCT tools from the field of image processing, and therefore they have fast computational speed that meets the requirement of real-time processing applications, which cannot be satisfied by any spatial domain models. The fastest models are PFT and PCT that spend the same time with almost the same performance, and then the order of time cost is: FDN, SR, PQFT, PFDN and pulsed PCA. Note that here the AQFT and compressed domain models are not listed since no comparison is given in [9, 10]. The model of pulsed PCA only aims at giving a reasonable explanation for frequency model. In practice, pulsed PCA is rarely used due to the existence of PCT (as PCT is faster for the same performance).

In most of the image databases, frequency domain approaches exhibit good consistency with psychophysical results as spatial domain models do, but they are short of biological basis. PFT and PQFT suggest that the phase spectrum represents the local edge information in the input image, while flatting the amplitude spectrum, the edge information with high frequency in general is just the focus of visual attention. The SR and PCT models seem to obey the same rule.

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