Saliency Detection through Nonlinear Metric Fusion

Zhi Yang

doi:10.4028/www.scientific.net/AMR.926-930.3692

Paper Titles

R(D) Function Iterative Algorithm Research
p.3673

Research of User-Based Collaborative Filtering Algorithm in Tag Recommendation
p.3677

Research on Prediction Method of Profile Grinding Surface Roughness Based on PSO-LS SVM Algorithm
p.3684

Research on the Path Optimization of Satellite Formation Reconfiguration Based on Gauss Pseudospectral Method
p.3688

Saliency Detection through Nonlinear Metric Fusion
p.3692

Study on the Stability of Virtual Network Mapping Algorithm Based on the Improved Genetic Algorithm
p.3696

The Improvement of the Three Dimensional Douglas-Peucker Algorithm
p.3701

The Optimization of Roll Force Model in Hot Strip Mill
p.3705

The Research of the Digital Image-Processing of Colorful Painting
p.3709

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Saliency Detection through Nonlinear Metric Fusion

Saliency Detection through Nonlinear Metric Fusion

Abstract:

In order to detect the saliency part in the picture of nature scene, usually, bottom-up saliency models use several feature information such as color and orientation, each feature is taken to compute the saliency map respectively, then these saliency maps are linearly combined to a final saliency map. However, how to fuse these features are still a difficult problem, linearly combined is not appropriate to the human visual system, so in this paper we propose a nonlinear metric fusion strategy with different features namely cross diffusion, it fuse the metric from diverse features which can be used to detect the saliency more appropriate.

You might also be interested in these eBooks

Progress in Applied Sciences, Engineering and Technology

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

3692-3695

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.3692

Citation:

Cite this paper

Online since:

May 2014

Authors:

Zhi Yang*

Keywords:

Bottom-up Saliency Model, Human Visual System, Nonlinear Fuse Strategy, Saliency Map

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Y. Karklin, M.S. Lewicki, Emergence of complex cell properties by learning to generalize in natural scenes, Nature 457 (7225), pp.83-86.

DOI: 10.1038/nature07481

Google Scholar

[2] B. Wang, J. Jiang, W. Wang, Z. Zhou, and Z. Tu, Unsupervised metric fusion by cross diffusion. ; In Proceedings of CVPR. 2012, 2997-3004.

Google Scholar

[3] J.G. Yu, J.W. Tian. Saliency detection using midlevel visual cues, Optics Letters 37 (2012) 4994-4996.

DOI: 10.1364/ol.37.004994

Google Scholar

[4] M. Cheng, G. Zhang, N.J. Mitra, X. Huang, S. Hu, Global contrast based salient region detection, In Proceedings of CVPR, 2011, pp.409-416.

DOI: 10.1109/cvpr.2011.5995344

Google Scholar

[5] R. Achanta, S. S. Hemami, F. J. Estrada, S. Sűsstrunk. Frequency-tuned salient region detection, In Proceedings of CVPR, 2009, pp.1597-1604.

DOI: 10.1109/cvpr.2009.5206596

Google Scholar

[6] Y. Zhai, M. Shah, Visual attention detection in video sequences using spatiotemporal cues, In Proceedings of ACM Multimedia, 2006, pp.815-824.

DOI: 10.1145/1180639.1180824

Google Scholar

[7] S. Goferman, L. Zelnik-Manor, A. Tal, Context-Aware Saliency Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (2012) 1915-(1926).

DOI: 10.1109/tpami.2011.272

Google Scholar

[8] J. Harel, C. Koch, P. Perona, Graph-Based Visual Saliency, In Proceedings of NIPS, 2006, pp.545-552.

Google Scholar