Integrating Phase Congruency and Two-dimensional Principal Component Analysis for Visual Saliency Prediction

In order to predict the pivotal visually attractive image regions more effectively, a novel saliency method using the phase congruency and the two-Dimensional Principal Component Analysis (2DPCA) is proposed in this paper. Firstly, the phase congruency is utilized to extract the most important feature points and the edge informations in the frequency domain, which is different from the conventional phase spectrum based methods. Then, after the quick shift superpixel based refinement, these features are incorporated with the local and global color contrast, to generate the low-level feature based saliency map. Then, the 2DPCA is adopted to extract the principal component vectors of image patches. The local and global distinctness between the different image patches in the principal component space are computed to get the pattern saliency map. Finally, these two complementary maps are integrated through the weighting strategy based on the spatial variance measure. The comparable experimental results on two benchmark eye tracking databases of the proposed method and 5 state-of-the-art methods show that the proposed method can predict eye fixation more accurately.