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

Saliency detection based on structural dissimilarity induced by image quality assessment model