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A K2 graph-based fusion model with manifold ranking for robot image saliency detection

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Abstract

Saliency detection is a key step in computer vision task, which can extract the interest regions in the image. It is widely used in image compression, image segmentation, object detection, and other fields, which has achieved remarkable results. There are some problems in the traditional image saliency detection algorithms, such as saliency object incomplete detection and inhomogeneity inside saliency object. This paper proposes a K2 graph-based fusion model with manifold ranking for robot image saliency detection. The proposed algorithm regards super-pixel as the node to construct K nearest neighbor graph model and K regular graph model. Manifold ranking algorithm is adopted to calculate the saliency value of super-pixel nodes on the two graph models, respectively. The saliency value of super-pixel nodes in each graph model is executed by a modified weight fusion approach to obtain the final saliency graph. Experiments on the three public data sets MSRA-10 K, SED2 and ECSSD are conducted. The proposed algorithm in this paper is compared with 14 state-of-the-art saliency detection methods. The average AUC and F score are more than 89% and 70%, respectively. The results show that the new algorithm can completely detect saliency objects, and the interior of saliency objects is uniform and smooth.

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Authors and Affiliations

  1. School of Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, 450000, China

    Dong Ye & Rui Yang

  2. Intelligent Information Processing and Control Engineering Technology Research Center of Henan Province, Zhengzhou, China

    Rui Yang

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  1. Dong Ye
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  2. Rui Yang
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Correspondence to Rui Yang.

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Ye, D., Yang, R. A K2 graph-based fusion model with manifold ranking for robot image saliency detection. Prog Artif Intell 11, 233–250 (2022). https://doi.org/10.1007/s13748-022-00280-8

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  • DOI: https://doi.org/10.1007/s13748-022-00280-8

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