Visual Saliency Models Based on Spectrum Processing

Some visual saliency models have been proposed to describe how the human visual system perceives and processes visual information. In this paper we describe four frequency domain visual saliency models based on new spectrum processing methods. The four saliency models are the Gamma Corrected Spectrum (GCS) model, the Gamma Corrected Log Spectrum (GCLS) model, the Gaussian Filtered Spectrum (GFS) model, and the Gaussian Filtered Log Spectrum (GFLS) model. A set of saliency map candidates are generated by inverse transform of a set of modified spectrums. An output saliency map is selected by minimizing the entropy among the set of saliency map candidates. Extension of these models are also described using various color spaces. Experimental results show that four extensions of our GCS, GCLS, GFS, and GFLS models are more accurate and efficient than some state-of-the-art saliency models in predicting eye fixation on standard image datasets.