Visual Saliency Detection via Sparse Residual and Outlier Detection
He Qing Tang, Chuanbo Chen, Xiaobing Pei · IEEE Signal Processing Letters · 2016
This letter proposes a bottom-up saliency model to predict eye fixation locations. Unlike traditional models that measure saliency by computing local or global distinctness, the proposed model considers saliency as the prediction error, because we believe that image patches or pixels with higher prediction error are more salient than others. The prediction error consists of both mispredicted error and unpredicted error. We propose a new algorithm called sparse residual to compute the mispredicted error. We then adopt outlier detection to compute the unpredicted error. Finally, we obtain the saliency map from merging the two results together via a guided filter. Extensive experiments on three benchmark databases show that our model is superior to 12 state-of-the-art models.