Empirical mode decomposition for saliency detection

We propose a novel method for saliency detection and attention selection inspired by processes in the human visual cortex. To mimic the varying spatial resolution of the human eye as well as the constant eye movements (saccades) and to model the effect of temporal adaptiveness, we use empirical mode decomposition and corresponding intrinsic mode functions (IMFs), instead of applying standard multi-scale framework as suggested in the state of the art. We derive IMFs between scales to calculate data driven center surround maps which locally reflect amount of information in the scene and we combine opposition color channels, luminosity information and orientation maps into a single saliency map calculated on IMFs. To equalize influence of different components contributing to the final saliency map, normalization steps are proposed. Finally, the MSER regions are calculated directly on the saliency map in order to obtain the most dominant points. We present results on both artificially generated images used in psychological experiments, natural images and application of our method for unknown object detection in robotics.