Saliency Detection Based on Adaptive Threshold Segmentation and Local Background Clues

TANG Hongmei; WU Shijing; GUO Yingchun; PEI Yanan

doi:10.11999/JEIT160984

Volume 39 Issue 7

Jul. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(7): 1592-1598

TANG Hongmei, WU Shijing, GUO Yingchun, PEI Yanan. Saliency Detection Based on Adaptive Threshold Segmentation and Local Background Clues[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1592-1598. doi: 10.11999/JEIT160984

Citation:

TANG Hongmei, WU Shijing, GUO Yingchun, PEI Yanan. Saliency Detection Based on Adaptive Threshold Segmentation and Local Background Clues[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1592-1598. doi: 10.11999/JEIT160984

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Saliency Detection Based on Adaptive Threshold Segmentation and Local Background Clues

doi: 10.11999/JEIT160984

1.
(School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China)
2.
(School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300401, China)

Funds:

Tianjin Science and Technology Project (14RCGFGX00846, 15ZCZDNC00130), Project of Natural Science Foundation of Hebei Province (F2015202239)

Received Date: 2016-09-29
Rev Recd Date: 2017-02-16
Publish Date: 2017-07-19

Abstract

Abstract

In order to improve the applicability for different types of image and integrity of the results, a saliency detection algorithm is proposed. It combines the adaptive threshold merging with a new background selection strategy. In the segmentation process, the color difference sequence is obtained by the selective fusion of RGB and LAB of adjacent blocks. Adaptive threshold is generated by inverse proportion model of block area parameter. Merging progress is done after the adaptive threshold comparison with the color difference sequence. In the background selection process, background regions are obtained by the local relative position of background-subject-background in the local area. The experimental results are optimized for edge. Compared with other algorithms, the saliency map of two values obtained does not need external threshold algorithm in this paper. Adaptive threshold merging can eliminate the details of objects in complex environments and can focus on the saliency comparison of the same level size objects.
- Saliency detection,
- Adaptive threshold,
- Adjacent color difference,
- Local background clues,
- Edge optimization

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References(20)

References

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