Saliency detection based on aggregated Wasserstein distance

Fengdong Sun; Wenhui Li

doi:10.1117/1.JEI.27.4.043014

16 July 2018 Saliency detection based on aggregated Wasserstein distance

Fengdong Sun, Wenhui Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 4, 043014 (July 2018). https://doi.org/10.1117/1.JEI.27.4.043014

Abstract

This paper proposes a saliency detection method based on the aggregated Wasserstein distance. A multidimensional Gaussian mixture model is used to model the superpixels, whereby the color information of different color spaces is combined. To overcome the lack of the closed-form solution for the Gaussian mixture model, we employ the aggregated Wasserstein distance to measure the perceptual similarity between different superpixels. The saliency value is then calculated from two aspects. First, the global saliency is computed through all the superpixels in the image using the aggregated Wasserstein distance. Second, the local saliency is computed in a lower range with the same measure. Finally, a saliency map is obtained by combining the two types of saliencies, and is filtered by spectral clustering all the superpixels. The experimental results show that the proposed method outperforms 11 recent exact algorithms on three widely used open datasets.

Citation Download Citation

Fengdong Sun and Wenhui Li "Saliency detection based on aggregated Wasserstein distance," Journal of Electronic Imaging 27(4), 043014 (16 July 2018). https://doi.org/10.1117/1.JEI.27.4.043014

Received: 29 March 2018; Accepted: 12 June 2018; Published: 16 July 2018

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