结合吸收Markov链和流行排序的显著性区域检测

doi:10.13229/j.cnki.jdxbgxb20170769

Abstract

Abstract:

Considering the low robustness of existing saliency detection methods in detecting complex nature images, a new saliency detection algorithm is proposed by combining the absorbing markov chain and manifold ranking. First, the entropy of the gray image is calculated to obtain the number of superpixels. Secondly, a two-stage saliency detection is used. In the first stage, the image edge superpixels are pre-processed, and then the first saliency map based on random walk on Absorbing Markov chain was computed by using background prior. In the second stage, the saliency scores computed by the first stage are used as foreground query seeds for manifold ranking in order to further optimize the saliency detection results. Experiments on publicly ASD and ECSSD databases demonstrate that, compared with the existing saliency detection methods, the proposed method can accurately highlight the saliency target, and effectively suppress the background area, while improve the performance evaluation of precision, recall and F-measure values.

Key words: information procession technology, saliency region detection, background prior, foreground query seed, absorbing Markov chain, manifold ranking

CLC Number:

TN919.8

SU Han-song,DAI Zhi-tao,LIU Gao-hua,ZHANG Qian-fang. Saliency region detection combining absorbing Markov chain and manifold ranking[J].Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1887-1894.

Figures/Tables 9

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References 27

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算法	代码	平均运行时间/s
SR	Matlab	0.615
IT	Matlab	0.611
GB	Matlab+C	0.735
FT	C	0.072
SF	C	0.202
LR	Matlab+C	0.40
RC	Matlab	0.752
GC	C	0.037
GS	C	2.0
AMC	Matlab+C	0.195
GMR	C	0.149
SO	Matlab+C	0.38
本文	Matlab+C	0.235

Saliency region detection combining absorbing Markov chain and manifold ranking

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