Image Saliency Detection Based on Background Constraint of Low Rank and Multi-cue Propagation

Hongmei TANG; Mengyue BAI; Liying HAN; Chunyang LIANG

doi:10.11999/JEIT200193

Volume 43 Issue 5

May 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(5): 1432-1440

Hongmei TANG, Mengyue BAI, Liying HAN, Chunyang LIANG. Image Saliency Detection Based on Background Constraint of Low Rank and Multi-cue Propagation[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1432-1440. doi: 10.11999/JEIT200193

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Hongmei TANG, Mengyue BAI, Liying HAN, Chunyang LIANG. Image Saliency Detection Based on Background Constraint of Low Rank and Multi-cue Propagation[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1432-1440. doi: 10.11999/JEIT200193

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Image Saliency Detection Based on Background Constraint of Low Rank and Multi-cue Propagation

doi: 10.11999/JEIT200193 cstr: 32379.14.JEIT200193

School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China

Funds: The Natural Science Foundation of Hebei Province (F2019202387)

Received Date: 2020-03-20
Rev Recd Date: 2020-09-13

Available Online: 2020-09-22

Publish Date: 2021-05-18

Abstract

Abstract

Considering the lack of subspace information digging and inaccurate propagation between nodes in existing saliency detection algorithm based on manifold ranking, an image saliency detection algorithm based on background constraint of low rank and multi-cue propagation is proposed. Primary visual priors such as color, location and boundary connectivity prior are fused to form a background high-level prior, which restrains the low rank decomposition of feature matrix and strengths the difference between low rank matrix and spares matrix, describes structural information of subspace fully to separate foreground and background efficiently. Cues of rareness perception and local smoothing are introduced for improving the reconstruction of propagation matrix, which improves the node’s propagation capacity that has low probability of color feature occurrence, enhances the relevance of local region, strengthens the properties of nodes accurately to obtain the compact and continuous salient regions. The experimental results on three benchmark datasets and the application to image retrieval demonstrate the efficiency and robustness of the proposed algorithm.
- Saliency detection,
- Background constraint of low rank,
- Multi-cue propagation,
- Rareness perception

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References(15)

References

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