Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
W. Qi, M. M. Cheng, A. Borji et al., “SaliencyRank: Two-stage manifold ranking for salient object detection,” Comput. Visual Media 1(4), 309320 (2015).
D. Zhou, J. Weston, A. Gretton et al., “Ranking on data manifolds,” Adv. Neural Inf. Process. Syst. 16, 169176 (2004).
C. Yang, L. Zhang, H. Lu et al., “Saliency detection via graph-based manifold ranking,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2013), pp. 31663173.
R. Achanta, A. Shaji, K. Smith et al., “SLIC superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Analy. Machine Intelligence 34(11), 22742282 (2012).
L. Zhou, Z. Yang, Q. Yuan et al., “Salient region detection via integrating diffusion-based compactness and local contrast,” IEEE Trans. Image Process. 24(11), 33083320 (2015).
C. Li, Y. Yuan, W. Cai et al., “Robust saliency detection via regularized random walks ranking,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 27102717.
R. Achanta, S. Hemami, F. Estrada et al., “Frequency-tuned salient region detection,” inIEEE Conference on Computer Vision and Pattern Recognition, CVPR (IEEE, 2009), pp. 15971604.
M. M. Cheng, N. J. Mitra, X. Huang et al., “Global contrast based salient region detection,” IEEE Trans. Pattern Anal. Mach. Int. 37(3), 569582 (2015).
J. Shi, Q. Yan, L. Xu et al., “Hierarchical image saliency detection on extended CSSD,” IEEE Trans. Pattern Anal. Mach. Int. 38(4), 717729 (2016).

Data & Media loading...


Article metrics loading...



Research focused on salient object region in natural scenes has attracted a lot in computer vision and has widely been used in many applications like object detection and segmentation. However, an accurate focusing on the salient region, while taking photographs of the real-world scenery, is still a challenging task. In order to deal with the problem, this paper presents a novel approach based on human visual system, which works better with the usage of both background prior and compactness prior. In the proposed method, we eliminate the unsuitable boundary with a fixed threshold to optimize the image boundary selection which can provide more precise estimations. Then, the object detection, which is optimized with compactness prior, is obtained by ranking with background queries. Salient objects are generally grouped together into connected areas that have compact spatial distributions. The experimental results on three public datasets demonstrate that the precision and robustness of the proposed algorithm have been improved obviously.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd