Multiview saliency detection based on improved multimanifold ranking

Yanjiao Shi; Yugen Yi; Ke Zhang; Jun Kong; Ming Zhang; Jianzhong Wang

doi:10.1117/1.JEI.23.6.061113

19 September 2014 Multiview saliency detection based on improved multimanifold ranking

Yanjiao Shi, Yugen Yi, Ke Zhang, Jun Kong, Ming Zhang, Jianzhong Wang

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 6, 061113 (September 2014). https://doi.org/10.1117/1.JEI.23.6.061113

Abstract

As an important problem in computer vision, saliency detection is essential for image segmentation, super-resolution, object recognition, and so on. We propose a saliency detection method for images. Instead of using contrast between salient regions and their surrounding areas, both cues from salient and nonsalient regions are considered in our study. Based on these cues, an improved multimanifold ranking algorithm is proposed. In our algorithm, features from multiple views are utilized and the different contributions of these multiview features are taken into account. Moreover, an iterative updating optimization scheme is explored to solve the objective function, during which the feature fusion is performed. After two-stage ranking by the improved multimanifold ranking algorithm, each image patch can be assigned a ranking score, which determines the final saliency. The proposed method is evaluated on four public datasets and is compared with the state-of-the-art methods. Experimental results indicate that the proposed method outperforms existing schemes both in qualitative and quantitative comparisons.

Citation Download Citation

Yanjiao Shi, Yugen Yi, Ke Zhang, Jun Kong, Ming Zhang, and Jianzhong Wang "Multiview saliency detection based on improved multimanifold ranking," Journal of Electronic Imaging 23(6), 061113 (19 September 2014). https://doi.org/10.1117/1.JEI.23.6.061113

Published: 19 September 2014

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