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Salient object detection via saliency bias and diffusion

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Abstract

Salient object detection aims to identify both spatial locations and scales of the salient object in an image. However, previous saliency detection methods generally fail in detecting the whole objects, especially when the salient objects are actually composed of heterogeneous parts. In this work, we propose a saliency bias and diffusion method to effectively detect the complete spatial support of salient objects. We first introduce a novel saliency-aware feature to bias the objectness detection for saliency detection on a given image and incorporate the saliency clues explicitly in refining the saliency map. Then, we propose a saliency diffusion method to fuse the saliency confidences of different parts from the same object for discovering the whole salient object, which uses the learned visual similarities among object regions to propagate the saliency values across them. Benefiting from such bias and diffusion strategy, the performance of salient object detection is significantly improved, as shown in the comprehensive experimental evaluations on four benchmark data sets, including MSRA-1000, SOD, SED, and THUS-10000.

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Acknowledgments

This work is supported partially by the National Natural Science Foundation of China under Grant 61203256 and 61233003, Natural Science Foundation of Anhui Province (1408085MF112), and the Fundamental Research Funds for the Central Universities (WK350000002 and WK3490000001).

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China (USTC), Hefei, 230027, China

    Dao Xiang & Zilei Wang

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  1. Dao Xiang
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  2. Zilei Wang
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Correspondence to Zilei Wang.

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Xiang, D., Wang, Z. Salient object detection via saliency bias and diffusion. Multimed Tools Appl 76, 6209–6228 (2017). https://doi.org/10.1007/s11042-016-3310-9

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