Image saliency detection via multi-scale statistical non-redundancy modeling

A multi-scale statistical non-redundancy modeling approach is introduced for saliency detection in images. The statistical non-redundancy of pixels at different wavelet sub-bands is characterized using a multi-dimensional lattice of non-parametric statistical models, thus taking into account image saliency at multiple scales. This identifies saliency in image attributes at multiple scales, and makes saliency detection strongly robust against noisy input images. Results based on images from a public database show that the proposed approach outperforms existing single and multi-scale approaches, particularly when dealing with noisy images.