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Volume 43 Issue 5
May  2021
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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
Citation: 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

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

doi: 10.11999/JEIT200193
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
  • 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.
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