3 April 2019 Saliency detection based on structural dissimilarity induced by image quality assessment model
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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 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.
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Yang Li and Xuanqin Mou "Saliency detection based on structural dissimilarity induced by image quality assessment model," Journal of Electronic Imaging 28(2), 023025 (3 April 2019). https://doi.org/10.1117/1.JEI.28.2.023025
Received: 16 June 2018; Accepted: 6 March 2019; Published: 3 April 2019
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Cited by 6 scholarly publications.
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Data modeling

Performance modeling

Image quality

Visual process modeling


Feature extraction

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