  The distinctiveness of image regions is widely used as the cue of saliency.
Generally, the distinctiveness is computed according to the absolute difference
of features. However, according to the image quality assessment (IQA) studies,
the human visual system is highly sensitive to structural changes rather than
absolute difference. Accordingly, we propose the computation of the structural
dissimilarity between image patches as the distinctiveness measure for saliency
detection. Similar to IQA models, the structural dissimilarity is computed
based on the correlation of the structural features. The global structural
dissimilarity of a patch to all the other patches represents saliency of the
patch. We adopt two widely used structural features, namely the local contrast
and gradient magnitude, into the structural dissimilarity computation in the
proposed model. Without any postprocessing, the proposed model based on the
correlation of either of the two structural features outperforms 11
state-of-the-art saliency models on three saliency databases.
