RGB-D Saliency Detection Based on Optimized ELM and Depth Level

Zhengyi LIU; Tianze XU

doi:10.11999/JEIT180826

Volume 41 Issue 9

Sep. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2224-2230

Zhengyi LIU, Tianze XU. RGB-D Saliency Detection Based on Optimized ELM and Depth Level[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2224-2230. doi: 10.11999/JEIT180826

Citation:

Zhengyi LIU, Tianze XU. RGB-D Saliency Detection Based on Optimized ELM and Depth Level[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2224-2230. doi: 10.11999/JEIT180826

Zhengyi LIU, Tianze XU. RGB-D Saliency Detection Based on Optimized ELM and Depth Level[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2224-2230. doi: 10.11999/JEIT180826

Citation:

Zhengyi LIU, Tianze XU. RGB-D Saliency Detection Based on Optimized ELM and Depth Level[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2224-2230. doi: 10.11999/JEIT180826

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RGB-D Saliency Detection Based on Optimized ELM and Depth Level

doi: 10.11999/JEIT180826

Zhengyi LIU^,,
Tianze XU

College of Computer Science and Technology, Anhui University, Hefei 230601, China

Funds: The Natural Science Foundation of Anhui Province (1908085MF182)

Received Date: 2018-08-22
Rev Recd Date: 2019-05-15

Available Online: 2019-06-03

Publish Date: 2019-09-10

Abstract

Abstract

Currently, many saliency-detection methods focus on 2D-image. But, these methods cannot be applied in RGB-D image. Based on this situation, new methods which are suitable for RGB-D image are needed. This paper presents a novel algorithm based on Extreme Learning Machine(ELM), feature-extraction and depth-detection. Firstly, feature-extraction is used for getting a feature, which contains 4-scale superpixels and 4096 dimensions. Secondly, according to the 4-sacle superpixels, the RGB, LAB and LBP feature of RGB image are computed, and LBE feature of depth image. Thirdly, weak salient map with LBE and dark-channel features are computed, and the foreground objects is strengthened in every circle. Fourthly, according to weak salient map, both foreground seeds and background seeds are chosen, and then, put these seeds into ELM to compute the first stage salient map. Finally, depth-detection and graph-cut are used for optimizing the first stage salient map and getting the second stage salient map.
- RGB-D saliency detection,
- Extreme Learning Machine(ELM),
- Process optimization,
- Multiple features,
- Depth level optimization

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References(31)

References

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