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A matting method based on full feature coverage

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Abstract

The sampling-based matting method is an important method for image matting. There are three key techniques in sampling-based matting: 1) how to build a sample-set; 2) how to travel a sample-set; 3) how to obtain a good sample-pair. Although sampling range has expanded from local to global, the existing approaches to build the sample-set are still limited within the boundary areas of a trimap. Therefore, some valid samples may be ignored if they are far away from the trimap boundary. The so-called global samplings are limited by this disadvantage. Our idea comes from the observation that the samples on both sides of a image edge of the whole image are most representative. Furthermore, in the color space, the pixels in the smooth region are very close to the pixels near the image edge. Based on the discoveries, we present a full feature coverage sampling method, which utilizes the edges as clues to search all possible samples of the whole image area. First, we adopt edge detection to find the edges of the image. Second, the pixels near the edges are gathered into the sample-set. Third, because the population of a complete sample-set is much larger than existing sample-set, we propose an optimization approach to accelerate travelling sample-sets. Fourth, we propose a selective strategy and adopt a propagation matting to enhance the results of sampling matting. Finally, the experimental results are tested on an online benchmark. The results show that the proposed method outperforms many other sampling-based matting methods. The ranking of our method is at the forefront.

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References

  1. Aksoy Y, Aydın TO, Pollefeys M (2017) Designing effective inter-pixel information flow for natural image matting, arXiv:1707.05055

  2. An F, Zhou X (2017) Bemd–sift feature extraction algorithm for image processing application. Multimed Tools Appl 76(11):13153–13172

    Article  Google Scholar 

  3. Chen Q, Li D, Tang C (2013) Knn matting. IEEE Trans Pattern Anal Mach Intell 35(9):2175–2188

    Article  Google Scholar 

  4. Chen X, Zou D, Zhiying Zhou S, Zhao Q, Tan P (2013) Image matting with local and nonlocal smooth priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1902–1907

  5. Chen X, Zou D, Zhou SZ, Zhao Q, Tan P (2013) Image matting with local and nonlocal smooth priors. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1902–1907

  6. Chen Y, He F, Wu Y, Hou N (2017) A local start search algorithm to compute exact hausdorff distance for arbitrary point sets. Pattern Recogn 67:139–148

    Article  Google Scholar 

  7. Cheng Y, He F, Wu Y (2016) D Meta-operation Conflict Resolution for Human-Human Interaction in Collaborative Feature-Based CAD systems. Clust Comput 19(1):237–253

    Article  Google Scholar 

  8. Cho D, Tai Y, Kweon I (2016) Natural image matting using deep convolutional neural networks. In: European Conference on Computer Vision. Springer, pp 626–643

  9. Chuang Y, Curless B, Salesin DH, Szeliski R (2001) A bayesian approach to digital matting. In: 2001. CVPR 2001. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, vol 2, pp II–II

  10. Ciregan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3642–3649

  11. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory

  12. Feng X, Liang X, Zhang Z (2016) A cluster sampling method for image matting via sparse coding. In: European Conference on Computer Vision. Springer, pp 204–219

  13. Gastal ES, Oliveira MM (2010) Shared sampling for real-time alpha matting. In: Computer Graphics Forum. Wiley Online Library, vol 29, pp 575–584

  14. Grady L, Schiwietz T, Aharon S, Westermann R (2005) Random walks for interactive alpha-matting. In: Proceedings of VIIP, pp 423–429

  15. He K, Rhemann C, Rother C, Tang X, Sun J (2011) A global sampling method for alpha matting. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 2049–2056

  16. He B, Wang G, Ruan Z, Yin X, Pei X, Lin X (2012) Local matting based on sample-pair propagation and iterative refinement. In: 2012 19th IEEE International Conference on Image Processing (ICIP). IEEE, pp 285–288

  17. He B, Wang G, Shi C, Yin X, Liu B, Lin X (2013) Iterative transductive learning for alpha matting. In: 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, pp 4282–4286

  18. He B, Wang G, Zhang C (2014) Iterative transductive learning for automatic image segmentation and matting with rgb-d data. J Vis Commun Image Represent 25 (5):1031–1043

    Article  Google Scholar 

  19. Hillman P, Hannah J, Renshaw D (2001) Alpha channel estimation in high resolution images and image sequences. In: 2001. CVPR 2001. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, vol 1, pp I–I

  20. Hullin M, Stamminger M, Weinkauf T (2016) Matting with sequential pair selection using graph transduction. In: Proceedings of tInternational Symposium on Vision, Modeling and visualization, VMV 2016, pp 111–118

  21. Johnson J, Rajan D, Cholakkal H (2014) Sparse codes as alpha matte, British Machine Vision Conference

  22. Johnson J, Varnousfaderani ES, Cholakkal H, Rajan D (2016) Sparse coding for alpha matting. IEEE Trans Image Process 25(7):3032–3043

    Article  MathSciNet  MATH  Google Scholar 

  23. Karacan L, Erdem A, Erdem E (2017) Alpha matting with kl-divergence-based sparse sampling. IEEE Trans Image Process 26(9):4523–4536

    Article  MathSciNet  MATH  Google Scholar 

  24. Lee P, Wu Y (2011) Nonlocal matting. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 2193–2200

  25. Levin A, Lischinski D, Weiss Y (2008) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242

    Article  Google Scholar 

  26. Li K, He F, Chen X (2016) Real-time object tracking via compressive feature selection. Front Comput Sci China 10(4):689–701

    Article  Google Scholar 

  27. Li C, Wang P, Zhu X, Pi H (2017) Three-layer graph framework with the sumd feature for alpha matting, Computer Vision and Image Understanding

  28. Li K, He F, Yu H (2017) A parallel and robust object tracking approach synthesizing bayesian learning and improved incremental subspace learning, Frontiers of Computer Science. https://doi.org/10.1007/s11704-018-6442-4

  29. Li K, He F, Yu HP, Chen X (2017) A correlative classifiers approach based on particle filter and sample set for tracking occluded target. Appl Math-A J Chin Univ 32(3):294–312

    Article  MathSciNet  Google Scholar 

  30. Li K, He F, Yu H (2018) Robust visual tracking based on convolutional features with illumination and occlusion handling. J Comput Sci Technol 33(1):223–236

    Article  Google Scholar 

  31. Lin Y, Wang H, Hsieh Y (2012) Image matting through a web browser. Multimed Tools Appl 61(3):551–570

    Article  Google Scholar 

  32. Lv X, He F, Cai W, Cheng Y (2017) A string-wise crdt algorithm for smart and large-scale collaborative editing systems. Adv Eng Inform 33(3):397–409

    Article  Google Scholar 

  33. Lv X, He F, Cai W (2018) Supporting selective undo of string-wise operations for collaborative editing systems. Futur Gener Comput Syst 82:41–62

    Article  Google Scholar 

  34. Lv X, He F, Cheng Y, Wu YQ (2018) A novel CRDT-based synchronization method for real-time collaborative CAD Systems. Adv Eng Inform 38:381–391

    Article  Google Scholar 

  35. Ni B, He F, Pan Y, Yuan Z (2016) Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy. Appl Math-a J Chin Univ Ser B 31(1):37–52

    Article  MathSciNet  MATH  Google Scholar 

  36. Porter T, Duff T (1984) Compositing digital images. In: ACM Siggraph Computer Graphics. ACM, vol 18, pp 253–259

    Article  Google Scholar 

  37. Rhemann C, Rother C, Gelautz M (2008) Improving color modeling for alpha matting.. In: BMVC, vol 1, pp 3

  38. Rhemann C, Rother C, Wang J, Gelautz M, Kohli P, Rott P (2009) A perceptually motivated online benchmark for image matting. In: 2009. CVPR IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 1826–1833

  39. Ruzon MA, Tomasi C (2000) Alpha estimation in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition Proceedings. IEEE, vol 1, pp 18–25

  40. Shahrian E, Rajan D, Price B, Cohen S (2013) Improving image matting using comprehensive sampling sets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 636–643

  41. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference On Evolutionary Computation Proceedings. IEEE, pp 69–73

  42. Shi Y, Au OC, Pang J, Tang K, Sun W, Zhang H, Zhu W, Jia L (2013) Color clustering matting. In: 2013 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp 1–6

  43. Sun J, Jia J, Tang C, Shum H (2004) Poisson matting. In: ACM Transactions on Graphics (ToG). ACM, vol 23, pp 315–321

    Article  Google Scholar 

  44. Sun J, He F, Chen Y, Chen X (2016) A multiple template approach for robust tracking of fast motion target. Appl Math-a J Chin Univ Ser B 31(2):177–197

    Article  MathSciNet  MATH  Google Scholar 

  45. Tan G, Chen H, Qi J (2016) A novel image matting method using sparse manual clicks. Multimed Tools Appl 75(17):10213–10225

    Article  Google Scholar 

  46. Varnousfaderani ES, Rajan D (2013) Weighted color and texture sample selection for image matting. IEEE Trans Image Process 22(11):4260–4270

    Article  MathSciNet  MATH  Google Scholar 

  47. Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval: A comprehensive study. In: Proceedings of the 22nd ACM international conference on Multimedia. ACM, pp 157–166

  48. Wang J, Cohen MF (2007) Optimized color sampling for robust matting. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR’07. IEEE, pp 1–8

  49. Wang Q, Yuan Y, Wang J, Yan P (2013) A Visual saliency by selective contrast. IEEE Trans Circ Syst Video Technol 23(7):1150–1155

    Article  Google Scholar 

  50. Wang Q, Yuan Y, Wang J, Yan P, Li X (2013) A Saliency detection by multiple-instance learning. IEEE Trans Cybern 43(2):660–672

    Article  Google Scholar 

  51. Wei X, Lu W, Xing W (2017) A rapid multi-source shortest path algorithm for interactive image segmentation. Multimed Tools Appl 67(20):21547–21563

    Article  Google Scholar 

  52. Wu Y, Peng X, Ruan K, Hu Z (2017) Improved image segmentation method based on morphological reconstruction. Multimed Tools Appl 76(19):19781–19793

    Article  Google Scholar 

  53. Wu Y, He F, Zhang D, Li X (2018) Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Trans Serv Comput 11 (2):341–353

    Article  Google Scholar 

  54. Xu N, Price B, Cohen S, Huang T (2017) Deep image matting, arXiv:1703.03872

  55. Yan X, Hao Z, Huang H (2018) Alpha matting with image pixel correlation. Int J Mach Learn Cybern 9(4):621–627

    Article  Google Scholar 

  56. Yan X, He F, Chen Y (2017) A novel hardware/software partitioning method based on position disturbed particle swarm optimization with invasive weed optimization. J Comput Sci Technol 32(2):340–355

    Article  MathSciNet  Google Scholar 

  57. Yan X, He F, Hou N, Ai H (2018) An efficient particle swarm optimization for large-scale hardware/software co-design system, vol 27

    Article  Google Scholar 

  58. Yu H, He F, Pan Y (2018) A novel region-based active contour model via local patch similarity measure for image segmentation. Multimed Tools Appl 77 (18):24097–24119

    Article  Google Scholar 

  59. Zhang Z, Zhu Q, Xie Y (2012) Learning Based Alpha Matting using Support Vector Regression. 2012 19th IEEE International Conference on Image Processing (ICIP), pp 2109–2112

  60. Zhang Z, Zhu Q, Xie Y (2012) A novel image matting approach based on naive bayes classifier.. In: International Conference on Intelligent Computing. Springer, Berlin, pp 433–441

    Google Scholar 

  61. Zhang D, He F, Han SH, Li X (2016) Quantitative optimization of interoperability during feature-based data exchange. Integr Comput-aided Eng 23 (1):31–50

    Article  Google Scholar 

  62. Zhou Y, Hui XU, Pan X et al (2016) Parsing main structures of indoor scenes from single RGB-d image, vol 10

    Article  Google Scholar 

  63. Zhou Y, He F, Qiu Y (2016) Optimization of parallel iterated local search algorithms on graphics processing unit. J Supercomput 72(6):2394–2416

    Article  Google Scholar 

  64. Zhou Y, He F, Qiu Y (2017) Dynamic strategy based parallel ant colony optimization on gpus for tsps. Sci China Inf Sci 60(6):068102

    Article  Google Scholar 

  65. Zhang K, Liu Q, Wu Y, Yang M. -H. (2016) Robust visual tracking via convolutional networks without training. IEEE Trans Image Process 25(4):1779–1792

    MathSciNet  MATH  Google Scholar 

  66. Zhang D, He F, Han S, Zou L, Wu Y, Chen Y (2017) An efficient approach to directly compute the exact hausdorff distance for 3d point sets. Integr Comput Aided Eng 24(3):261–277

    Article  Google Scholar 

  67. Zheng Y, Kambhamettu C (2009) Learning based digital matting. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE, pp 889–896

  68. Zhou Y, He F, Hou N, Qiu Y (2018) Parallel ant colony optimization on multi-core simd cpus. Futur Gener Comput Syst 79(2):473–487

    Article  Google Scholar 

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Acknowledgments

We would like to thank all the anonymous reviewers for their valuable comments. This work is supported by the National Natural Science Foundation of China(Grant No. 61472289) and the National Key Research and Development Project(Grant No.2016YFC0106305).

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  1. School of Computer Science, Wuhan University, Wuhan, China

    Xiao Chen, Fazhi He & Haiping Yu

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  1. Xiao Chen
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Correspondence to Fazhi He.

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Chen, X., He, F. & Yu, H. A matting method based on full feature coverage. Multimed Tools Appl 78, 11173–11201 (2019). https://doi.org/10.1007/s11042-018-6690-1

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