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Saliency Detection Based on Hierarchical Graph Integration
Journal of Frontiers of Computer Science and Technology (2016)
Visual saliency detection has many applications in object segmentation, adaptive compression, object recognition
and so on. It has a challenge to accurately detect the most important regions from the nature images. This paper
proposes a hierarchical saliency detection algorithm based on manifold ranking for the problem of low detection accuracy
with the ignorance of the spatial layout information in the existing graph-based manifold ranking algorithms.
Firstly, the super pixels by multi- scale analysis are done for the decomposition of the input image. Secondly, the
boundary prior is used to compute the relevance ranking score between nodes by manifold ranking. Finally, by analyzing
saliency cues from the multiple level graph model, the final saliency map is generated by combining the
saliency maps. The experimental results on ASD, CSSD, ECSSD and SOD image datasets, demonstrate that the detection
precision outperforms the nine state-of-the-art algorithms while still preserving high recall.
  • saliency detection; manifold ranking; super-pixel segmentation; multi-scale analysis; graph model
Publication Date
Fall October 1, 2016
Citation Information
"Saliency Detection Based on Hierarchical Graph Integration" Journal of Frontiers of Computer Science and Technology (2016)
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