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3 April 2019Saliency detection based on structural dissimilarity induced by image quality assessment model

Yang Li, Xuanqin Mou

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Yang Li,¹ Xuanqin Mou¹

¹Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong Univ. (China)

Funded by:National Key Research and Development Program of China, National Natural Science Foundation of China

Journal of Electronic Imaging, Vol. 28, Issue 2, 023025 (April 2019).https://doi.org/10.1117/1.JEI.28.2.023025

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Abstract

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.

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Yang Li andXuanqin 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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