Volume 16, Issue 1 (5-2019)
JSDP 2019, 16(1): 75-90 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Amintoosi M. Enhancement of Learning Based Image Matting Method with Different Background/Foreground Weights. JSDP 2019; 16 (1) :75-90
URL: http://jsdp.rcisp.ac.ir/article-1-797-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-797-en.html
Hakim Sabzevari University
Abstract: (3392 Views)
The problem of accurate foreground estimation in images is called Image Matting. In image matting methods, a map is used as learning data, which is produced by those pixels that are definitely foreground, definitely background ,and unknown. This three-level pixel map is often referred to as a trimap, which is produced manually in alpha matte datasets. The true class of unknown pixels will be estimated by minimizing of an objective function. Several methods for image matting has been proposed. The learning–based method is one the pioneering works which is the basis of many other approaches in the field of image matting. In this method it is assumed that each pixel’s alpha value is a linear combination of its associated neighboring pixels. A Laplacian matrix in the objective function shows the similarity of the pixels. The coefficients of the linear combination are estimated with a local learning process by minimizing a quadratic cost function. The method of Lagrange multiplier and ridge regression technique are used for estimation of alpha values. In this objective function the violation of the predefined training pixels’ alpha values from their true values is controlled by a penalty term. Considering this coefficient as infinity, forces the matte (alpha) value to be 1 for the labeled foreground pixels and 0 for background. The weight of this penalty term still was taken equal for all training samples. In this paper the performance of the matting method is increased by considering different weights for different learning pixels. The good performance of the proposed method is demonstrated in two applications. The first application is improving the quality of a text extraction method and the second application is enhancement of an eye retinal segmentation system. In the first application, a Persian text which is fused onto a textured background is extracted by a thresholding method. After that the segmented output is enhanced by the proposed matting method. In the second application, segmentation is done with an existing vessel extraction method. The edges’ pixels of detected vessels that may be classified inaccurately are classified by the proposed image matting method. Subjective and objective comparisons show the better performance of the proposed method.
Keywords: Image Matting, Image Segmentation, Machine Learning, Text Extraction, Retinal Vessel Segmentation
Type of Study: Research |
Subject:
Paper
Received: 2018/01/4 | Accepted: 2019/01/26 | Published: 2019/06/10 | ePublished: 2019/06/10
Received: 2018/01/4 | Accepted: 2019/01/26 | Published: 2019/06/10 | ePublished: 2019/06/10
References
1. [1] J. Wang and M. F. Cohen, "Image and video matting: A survey," Found. Trends. Comput. Graph. Vis., vol.3, pp.97-175, Jan. 2007. [DOI:10.1561/0600000019]
2. [2] J. Johnson, D. Rajan, and H. Cholakkal, "Sparse codes as alpha matte," in BMVC, BMVA Press, 2014. [DOI:10.5244/C.28.74]
3. [3] Q. Chen, D. Li, and C.-K. Tang, "KNN mat-ting," Pattern Analysis and Machine Intelli-gence, IEEE Transactions on, vol.35, pp.2175-2188, Sept 2013. [DOI:10.1109/TPAMI.2013.18] [PMID]
4. [4] J. Gao, M. Paul, and J. Liu, "The image mat-ting method with regularized matte," IEEE Computer Society in ICME, , pp.550-555, 2012. [DOI:10.1109/ICME.2012.182]
5. [5] P. G. Lee and Y. Wu, "L1 matting," IEEE in ICIP, pp.4665-4668, , 2010.
6. [6] I. Choi, S. Kim, M. S. Brown, and Y. W. Tai, "A learning-based approach to reduce jpeg ar-tifacts in image matting," in 2013 IEEE International Conference on Computer Vision, pp.2880-2887, Dec 2013. [DOI:10.1109/ICCV.2013.358] [PMCID]
7. [7] Y. Zheng and C. Kambhamettu, "Learning based digital matting.," in 12th International Conference on Computer Vision, (Kyoto), pp.889-896, 2009.
8. [8] A. Levin, D. Lischinski, and Y. Weiss, "A closed-form solution to natural image matting," IEEE Trans. Pattern Anal. Mach. Intell., vol.30, no.2, pp.228-242, 2008. [DOI:10.1109/TPAMI.2007.1177] [PMID]
9. [9] J. Sun, J. Jia, C.-K. Tang, and H.-Y. Shum, "Poisson matting," ACM Trans. Graph., vol.23, pp.315-321, Aug. 2004. [DOI:10.1145/1015706.1015721]
10. [10] Z. Zhang, Q. Zhu, and Y. Xie, "Learning based alpha matting using support vector regression, " in 2012 ,19th IEEE International Conference on Image Processing, pp. 2109-2112, Sept 2012. [DOI:10.1109/ICIP.2012.6467308]
11. [11] X. Li and Q. Cui, Parallel Accelerated Matting Method Based on Local Learning, pp.152-162 Cham: Springer International Publishing, 2017. [DOI:10.1007/978-3-319-54181-5_10]
12. [12] K. Jung, K.Kim, andA. Jain, "Text information extraction in images and video: a survey," Pattern Recognition, vol.37, pp.977-997, 5 2004. [DOI:10.1016/j.patcog.2003.10.012]
13. [13] N. Otsu, "A Threshold Selection Method from Gray-level Histograms," IEEE Transactions on Systems, Man and Cybernetics, vol.9, no.1, pp.62-66, 1979. [DOI:10.1109/TSMC.1979.4310076]
14. [14] M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. Rudnicka, C. Owen, and S. Barman, "Blood vessel segmentation methodo-logies in retinal images - a survey," Comput. Methods Prog. Biomed., vol. 108, pp.407-433, Oct. 2012. [DOI:10.1016/j.cmpb.2012.03.009] [PMID]
15. [15] M. T. Dehkordi, S. Sadri, and A. Doosthoseini, "A review of coronary vessel segmentation algo-rithms.," Journal of Medical Signals & Sensors, vol.1, no.1, pp.49-54, 2011.
16. [16] P. Talwar , M. D. Gupta, "Alpha-matting based retinal vessel extraction," United States Patent Application 20160163041, June 2016.
17. [17] P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, "Fast retinal vessel detection and measurement using wavelets and edge location refinement.," PloS one, vol.7, no.3, 2012. [DOI:10.1371/journal.pone.0032435] [PMID] [PMCID]
18. [18] J. I. Orlando , M. Blaschko, "Learning fullyconnected CRFs for blood vessel segmentation in retinal images," in Medical Image Computing and Computer-Assisted Intervention -MIC-CAI 2014, vol. 8673 of Lecture Notes in Computer Science, pp.634-641, Springer, 2014. [DOI:10.1007/978-3-319-10404-1_79] [PMID]
19. [19] V. M. Saffarzadeh, A. Osareh, and B. Shadgar, "Vessel segmentation in retinal images using multi-scale line operator and k-means clustering," Journal of Medical Signals & Sensors, vol. 4, no.2, pp.122-129, 2014.
20. [20] G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, "Trainable COSFIRE filters for vessel delineation with application to retinal images," Medical Image Analysis, vol.19, no.1, pp.46-57, 2015. [DOI:10.1016/j.media.2014.08.002] [PMID]
21. [21] M. Zardadi ,and N. Mehrshad, "A New Approach to Retinal Vessel Segmentation by Using Computational Model of Simple Cells in Primary Visual Cortex". JSDP, vol. 13, no. 1, pp. 127-138, 2016.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
