RARE2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis

For the last decades, computer-based visual attention models aiming at automatically predicting human gaze on images or videos have exponentially increased. Even if several families of methods have been proposed and a lot of words like centre-surround difference, contrast, rarity, novelty, redundancy, irregularity, surprise or compressibility have been used to define those models, they are all based on the same and unique idea of information innovation in a given context.In this paper, we propose a novel saliency prediction model, called RARE2012, which selects information worthy of attention based on multi-scale spatial rarity. RARE2012 is then evaluated using two complementary metrics, the Normalized Scanpath Saliency (NSS) and the Area Under the Receiver Operating Characteristic (AUROC) against 13 recently published saliency models. It is shown to be the best for NSS metric and second best for AUROC metric on three publicly available datasets (Toronto, Koostra and Jian Li).Finally, based on an additional comparative statistical analysis and the effect-size Hedge' g^? measure, RARE2012 outperforms, at least slightly, the other models while considering both metrics on the three databases as a whole.