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Saliency detection based on salient edges and remarkable discriminating for superpixel pairs

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Abstract

Saliency detection is an essential pre-processing step for intensifying image objects in many computer vision fields. Other than most bottom-up methods, in this paper, we propose a novel saliency model based on high-level image edges and low-level feature contrast. Edges are inherent features for an image, and it can locate the object via a salient selection. With this theory, an accurate object contour is generated after two salient discriminations for edge-contiguous superpixel couples. After position is confirmed, foreground and background of the image can be divided by the contour. With an ingrowth model, we then obtain a foreground (or background) seeds referencing spatial adjacent relation. According to the seed set, a homologous saliency map is computed via a seed-based saliency approach, which is proposed on the basis of affinity matrix. Compared with some pre-existing algorithms, low-level information is utilized purposefully by salient edges in the presented method, which makes the extracted fore regions more precise.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China

    Zhengping Hu, Zhenbin Zhang, Zhe Sun & Shuhuan Zhao

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  1. Zhengping Hu
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  2. Zhenbin Zhang
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  3. Zhe Sun
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  4. Shuhuan Zhao
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Correspondence to Zhenbin Zhang.

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Hu, Z., Zhang, Z., Sun, Z. et al. Saliency detection based on salient edges and remarkable discriminating for superpixel pairs. Multimed Tools Appl 77, 5949–5968 (2018). https://doi.org/10.1007/s11042-017-4508-1

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  • DOI: https://doi.org/10.1007/s11042-017-4508-1

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