Salient Object Detection via Multi-feature Diffusion-based Method

YE Feng; HONG Siting; CHEN Jiazhen; ZHENG Zihua; LIU Guanghai

doi:10.11999/JEIT170827

Volume 40 Issue 5

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(5): 1210-1218

YE Feng, HONG Siting, CHEN Jiazhen, ZHENG Zihua, LIU Guanghai. Salient Object Detection via Multi-feature Diffusion-based Method[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1210-1218. doi: 10.11999/JEIT170827

Citation:

YE Feng, HONG Siting, CHEN Jiazhen, ZHENG Zihua, LIU Guanghai. Salient Object Detection via Multi-feature Diffusion-based Method[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1210-1218. doi: 10.11999/JEIT170827

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Salient Object Detection via Multi-feature Diffusion-based Method

doi: 10.11999/JEIT170827

Funds:

The National Natural Science Foundation of China (61671077, 61463008), The Natural Science Foundation of Fujian Province (2017J01739), The Scientific Research Fund of Fujian Education Department (JA15136), The Teaching Reform Project of Fujian Normal University (I201602015)

Received Date: 2017-08-23
Rev Recd Date: 2018-01-11
Publish Date: 2018-05-19

Abstract

Abstract

Most existing salient object detection methods based on diffusion theory usually only use one feature of image to construct graph and diffusion matrix, and ignore the possibility that salient objects appear at the border regions of the image. In this paper, a diffusion method based on the multi-layer features of image is proposed to detect salient objects. Firstly, the seed nodes are selected by adopting the high-level prior method, which is composed of background prior, color prior, and location prior. Then, the initial saliency map is obtained by propagating the saliency information carried by the selected seed nodes to each nodes via the diffusion matrix constructed by the low-level feature of the image, and used as the middle-level feature of image. The diffusion matrices are re-synthesized again by the middle-level feature and the high-level feature of the image, and the middle-level saliency map and the high-level saliency map are obtained by the diffusion-based method respectively. The final saliency map is obtained by nonlinearly combining the the middle-level and high-level saliency map. Results on three datasets, MSRA10K, DUT-OMRON and ECSSD, show that the proposed method achieves superior performance compared with the four state-of-art methods in terms of three evaluation metrics.
- Salient object detection,
- Diffusion method,
- Multi-layer features,
- High-level prior

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References(25)

References

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