Color decorrelation helps visual saliency detection

Abstract

We present how color decorrelation allows visual saliency models to achieve higher performance when predicting where people look in images. For this purpose, we decorrelate the color information of each image, which leads to an image-specific color space with decorrelated color components. This way, we are able to improve the performance of several well-known visual saliency algorithms such as, for example, Itti and Koch's model and Hou and Zhang's spectral residual saliency. We show the advantage of color decorrelation on three eye-tracking datasets (Kootstra, Toronto, and MIT) with respect to three evaluation measures (AUC, CC, and NSS).