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Saliency Detection with VORONOI Diagram

by Dao Nam Anh, Nguyen Huu Quynh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 12
Year of Publication: 2015
Authors: Dao Nam Anh, Nguyen Huu Quynh
10.5120/20798-3468

Dao Nam Anh, Nguyen Huu Quynh . Saliency Detection with VORONOI Diagram. International Journal of Computer Applications. 118, 12 ( May 2015), 27-34. DOI=10.5120/20798-3468

@article{ 10.5120/20798-3468,
author = { Dao Nam Anh, Nguyen Huu Quynh },
title = { Saliency Detection with VORONOI Diagram },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 12 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 27-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number12/20798-3468/ },
doi = { 10.5120/20798-3468 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:01:30.993105+05:30
%A Dao Nam Anh
%A Nguyen Huu Quynh
%T Saliency Detection with VORONOI Diagram
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 12
%P 27-34
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Many applications are serviced by the Voronoi tessellation required to split image into Voronoi regions. An automatic method to learn and detect salient region for color image with support of the Voronoi diagram is presented. Salient regions are modeled as flexible circumstance corresponding to centers of mass. The centers are predicted by local contrast-based representation with local maxima. Results are demonstrated that are very competitive with other recent saliency map detection schemes and show robustness to capture visual attention objects. Our major contributions are the local maxima based method for allocation of Voronoi centroids and the Gaussian-based filter for estimating attention degrees. To show the effectiveness of the approach, saliency maps are detected for images of MSRA saliency object database by some state-of-the-art methods. The strengths and the weaknesses of the approach are considered, with a special focus on the context based salient regions ? a challenging task which can be found in wide range of applications addressed in computer vision.

References
  1. G. Voronoi. Nouvelles applications des paramètres continus à la théorie des formes quadratiques. Journal für die Reine und Angewandte Mathematik, 133:97178,1907.
  2. Chew, L. P. 1986. Building Voronoi diagrams for convex polygons in linear expected time. Technical Report PCS-TR90-147, Dept. Math. Comput. Sci. , Dartmouth College, Hanover.
  3. Franz Aurenhammer, Voronoi diagrams - a survey of a fundamental geometric data structure, ACM computing surveys, vol. 23, no. 3, 1991, pages 345-405.
  4. Atsuyuki Okabe, Barry Boots, Kokichi Sugihara & Sung Nok Chiu (2000). Spatial tessellations – concepts and applications of Voronoi diagrams. 2nd edition. John Wiley, 2000, 671 pages.
  5. S. Hasegawa, H. Imai, M. Inaba, N. Katoh, and J. Nakano, Efficient algorithms for variance-based k-clustering, in Proceedings of the First Pacific Conference on Computer Graphics and Applications, World Scientific, River Edge, NJ, 1993, pp. 75–89.
  6. M. Inaba, H. Imai, and N. Katoh, Experimental results of randomized clustering algorithms, in Proceedings of the 12th ACM Symposium on Computational Geometry: Communication Section, 1996.
  7. Thouis R. Jones, A Carpenter, and P Golland. 2005. Voronoi-Based segmentation of cells on image manifolds. In Proc of the First inter. Conf. on Com. Vision for Biomed Image App. (CVBIA'05), pp 535-543.
  8. Q. Du and X. Wang, Centroidal Voronoi tessellation based algorithms for vector fields visualization and segmentation, Proc. IEEE Visualization, pp. 43-50, 2004.
  9. ]M. Tüceryan and Anil K. Jain. 1990. Texture segmentation using Voronoi polygons. IEEE Trans. Pattern Anal. Mach. Intell. 12, 2 (1990), 211-216.
  10. Niranjan Mayya and V. T. Rajan. 1996. Voronoi diagrams of polygons: a framework for shape representation. J. Math. Im. Vis. 6, 4 (1996), 355-378.
  11. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. On PAMI, 20(11), 1998.
  12. J. Han, K. Ngan, M. Li, and H. Zhang. Unsupervised extraction of visual attention objects in color images. IEEE Transactions on Circuits and Systems for Video Technology, 16(1):141. 145, 2006.
  13. Zund, F. , Pritch, Y. , Sorkine-Hornung, A. , Mangold, S. , Content-aware compression using saliency-driven image retargeting, Image Processing (ICIP), 2013.
  14. A. Shokoufandeha, I. Marsicb, Sven J. Dickinsona, View-based object recognition using saliency maps, Image and Vision Computing 17 (1999) 445–460.
  15. P. Wang, J. Wang, G. Zeng, J. Feng, Salient object detection for searched web images via global saliency, (CVPR), 2012.
  16. G. Schillaci, S. Bodiroža, V. Hafner, Evaluating the effect of saliency detection and attention manipulation in human-robot interaction, Int. J. of Social Robotics, 2013, Vol. 5, Is. 1, pp 139-152.
  17. A. Cheddad, D. Mohamad and A. Abd Manaf, Exploiting Voronoi diagram properties in face segmentation and features extraction, Pattern Recognition, 41 (12) (2008) 3842-3859, Elsevier Science.
  18. Sinclair, D. : Voronoi seeded colour image segmentation. Technical Report 3, AT&T Laboratories Cambridge (1999).
  19. P. Beeson, N. K. Jong, and B. Kuipers, Towards autonomous topological place detection using the extended Voronoi graph, IEEE Inter. Conference on Robotics and Automaton (ICRA), 2005.
  20. Y. Zhou, L. Ju, Y. Cao, Jarrell W. Waggoner, Y. Lin, J. Simmons, S. Wang. Edge-weighted centroid Voronoi tessellation with propagation of consistency constraint for 3d grain segmentation in microscopic superalloy images. CVPR Workshops 2014: 258-265.
  21. Narendra Ahuja, Sinisa Todorovic, Connected segmentation tree –a joint representation of region layout and hierarchy, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2008.
  22. Arbelaez, P. A. , & Cohen, L. D. (2003). Generalized Voronoi tessellations for vector-valued image segmentation. Proc. 2nd IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision (VLSM'03) (pp. 49-56).
  23. H. Chang, Q. Yang, B. Parvin, Segmentation of heterogeneous blob objects through voting and level set formulation, Pattern Recognit Lett. 2007, 28(13).
  24. Sébastien Bougleux, Gabriel Peyré, Laurent D. Cohen. Anisotropic geodesics for perceptual. grouping and domain meshing. ECCV 2008.
  25. Matthew Cook, Probabilistic reasoning and decision making in sensory-motor systems. The Neuromorphic Engineer (2009).
  26. Ran Fan, Xiaogang Jin, and Charlie C. L. Wang, Multi-region segmentation based on compact shape prior. IEEE Trans. on Automation Science and Engineering, 2014.
  27. Tie Liu, Jian Sun, Nan-Ning Zheng, Xiaoou Tang, Heung-Yeung Shum. Learning to detect a salient object. In Proc. IEEE Cont. on Computer Vision and pattern Recognition (CVPR), 2007.
  28. N. Riche, M. Mancas, M. Duvinage, M. Mibulumukini, B. Gosselin, T. Dutoit, RARE2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis. Sig. Proc. : Image Comm. 28(6): 642-658 (2013).
  29. Hae Jong Seo, and Peyman Milanfar, Static and space-time visual saliency detection by self-resemblance, J. Vision 9(12):15,1-27.
  30. R. Margolin, L. Zelnik-Manor, A. Tal. Saliency for image manipulation, The Visual Computer, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Voronoi tessellation salient region.

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