Skip to main content
SpringerLink
Log in

Robust trimap optimization algorithm based on Superpixel Citation-KNN

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose a trimap optimization method that can optimize manually created rough trimaps. Most matting algorithms require the user to intervene by using a trimap to generate an alpha mask from the input image. An accurate trimap guarantees a high-quality alpha mask. However, creating a trimap is undoubtedly a very tedious task, and a rough trimap will reduce the accuracy of the matting algorithm. We optimize the manually created trimap based on the local weighted Citation-KNN algorithm, which enables the matting method to obtain results quickly and accurately. The experiments performed show that the method proposed in this paper can better optimize rough trimap created by manual manipulation, thus improving the alpha mask estimation accuracy. We validate our results by replacing our optimized trimap with a manually created trimap while using the same image matting algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Acevedo D, Negri P, Buemi ME, Fernàndez FG, Mejail M (2017) A Citation k-NN Approach for Facial Expression Recognition. Iberoamerican CongressPattern Recogn:1–9

  2. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Su̇sstrunk S (2012) SLIC Superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282

    Article  Google Scholar 

  3. Agarwal PK, Fox K, Nath A, Sidiropoulos A, Wang Y (2018) Computing the Gromov-Hausdorff distance for metric trees. ACM Trans Algorithm 14(2):24

    Article  MathSciNet  Google Scholar 

  4. Aksoy Y, Aydin TO, Pollefeys M (2017) Designing effective interpixel information flow for natural image matting. IEEE Conf Comput Vis Pattern Recogn:228–236

  5. Al-Kabbany A, Dubois E (2015) A novel framework for automatic trimap generation using the Gestalt laws of grouping. Vis Inf Process Commun VI:94100G-94100G-9

  6. Alelyani S, Tang J, Liu H (2013) Feature selection for clustering: a review. Data Clustering: Chapman and Hall/CRC, pp 29–60

  7. Amin B, Riaz M, Ghafoor A (2020) Automatic aircraft extraction using video matting and frame registration. IET Image Process 14(8):1628–1635

    Article  Google Scholar 

  8. Amin B, Riaz M, Ghafoor A (2021) Fuzzy based iterative matting technique for underwater images. IET Image Processing

  9. Cai S, Zhang X, Fan H, Huang H, Liu J, Sun J (2019) Disentangled image matting. IEEE Conf Comput Vis Pattern Recogn:8819–8828

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

    Article  Google Scholar 

  11. Cho D, Kim S, Tai Y-W, Kweon I (2017) Automatic trimap generation and consistent matting for light-field images. IEEE Trans Pattern Anal Mach Intell 39(8):1504–1517

    Article  Google Scholar 

  12. Cho D, Tai Y-W, Kweon I (2016) Natural image matting using deep convolutional neural networks. Eur Conf Comput Vis:626–643

  13. Chuang Y, Curless B, Salesin DH, Szeliski R (2001) A bayesian approach to digital matting. IEEE Conf Comput Vis Pattern Recogn:264–271

  14. Dai Y, Lu H, Shen C (2020) Learning Affinity-Aware Upsampling for Deep Image Matting, arXiv:2011.14288

  15. Ding J, Cheng H, Xian M, Zhang Y, Xu F (2015) Local-weighted citation-kNN algorithm for breast ultrasound image classification. Optik 126(24):5188–5193

    Article  Google Scholar 

  16. Gastal ES, Oliveira MM (2010) Shared sampling for real-time alpha matting. Comput Graph Forum 29(2):575–584

    Article  Google Scholar 

  17. Ghosh D, Bandyopadhyay S (2015) A fuzzy citation-KNN algorithm for multiple instance learning. IEEE Int Conf Fuzzy Syst:1–8

  18. Guillaumin M, Verbeek J, Schmid C (2010) Multiple instance metric learning from automatically labeled bags of faces. Eur Conf Comput Vis:634–647

  19. Gupta V, Raman S (2016) Automatic trimap generation for image matting. Int Conf Signal Inf Process:1–5

  20. He K, Rhemann C, Rother C, Tang X, Sun J (2011) A global sampling method for alpha matting. IEEE Conf Comput Vis Pattern Recogn:2049–2056

  21. He K, Sun J, Tang X (2010) Fast matting using large kernel matting laplacian matrices. IEEE Conf Comput Vis Pattern Recogn:2165–2172

  22. Hou Q, Liu F (2019) Context-aware image matting for simultaneous foreground and alpha estimation. IEEE Int Conf Comput Vis:4130–4139

  23. Hsieh CL, Lee MS (2013) Automatic trimap generation for digital image matting. Signal Inf Process Assoc Ann Summit Conf:1–5

  24. Hu H, Pang L, Shi ZJK-BS (2016) Image matting in the perception granular deep learning. Knowl-Based Syst 102:51–63

    Article  Google Scholar 

  25. Huttenlocher DP, Rucklidge WJ, Klanderman GA (1992) Comparing images using the Hausdorff distance under translation. CIEEE Conf Comput Vis Pattern Recogn:654–656

  26. Jaime M, et al. (2015) A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest X-rays. IEEE Trans Med Imaging 34(1):179–92

    Article  Google Scholar 

  27. Jiang L, Cai Z, Wang D, Zhang H (2014) Cybernetics, Bayesian Citation-KNN with distance weighting. Int J Mach Learn Cybern 5(2):193–199

    Article  Google Scholar 

  28. Katsamanis A, Gibson J, Black MP, Narayanan SS (2011) Multiple instance learning for classification of human behavior observations. Int Conf Affect Comput Intell Interact:145–154

  29. Kyurkchiev N, Markov S (2016) On the Hausdorff distance between the Heaviside step function and Verhulst logistic function. J Math Chem 54(1):109–119

    Article  MathSciNet  Google Scholar 

  30. Lee P, Wu Y (2011) Nonlocal matting. IEEE Conf Comput Vis Pattern Recogn:2193–2200

  31. 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 

  32. Levin A, Rav-Acha A, Lischinski D (2008) Spectral matting. IEEE Trans Pattern Anal Mach Intell 30(10):1699–1712

    Article  Google Scholar 

  33. Li Y, Lu H (2020) Natural image matting via guided contextual attention. Proc AAAI Conf Artif Intell 34(07):11450–11457

    Google Scholar 

  34. Li C, Wang P, Zhu X, Pi H (2017) Three-layer graph framework with the sumD feature for alpha matting. Comput Vis Image Underst 162:34–45

    Article  Google Scholar 

  35. Li H, Zhu W, Jin H, Ma Y (2021) Illumination-Invariant And Real-Time Green-Screen keying using deeply guided linear models. Symmetry 13 (8):1454

    Article  Google Scholar 

  36. Lin S, Ryabtsev A, Sengupta S, Curless BL, Seitz SM, Kemelmacher-Shlizerman I (2021) Real-time high-resolution background matting. IEEE Conf Comput Vis Pattern Recogn, pp 8762–8771

  37. Lu H, Dai Y, Shen C, Xu S (2019) Indices matter: Learning to index for deep image matting. IEEE Conf Comput Vis Pattern Recogn:3266–3275

  38. Lutz S, Amplianitis K, Smolic A (2018) AlphaGAN: Generative adversarial networks for natural image matting, arXiv:1807.10088

  39. Lutz S, Amplianitis K, Smolic A (2018) Alphagan: Generative adversarial networks for natural image matting. arXiv:1807.10088

  40. Qiao Y, Liu Y, Yang X, Zhou D, Xu M, Zhang Q, Wei X (2020) Attention-guided hierarchical structure aggregation for image matting. IEEE Conf Comput Vis Pattern Recogn:13676–13685

  41. Quellec G, Cazuguel G, Cochener B, Lamard M (2017) Multipleinstance learning for medical image and video analysis. IEEE Rev Biomed Eng 10:213–234

    Article  Google Scholar 

  42. Rhemann C, Rother C, Gelautz M (2008) Improving color modeling for alpha matting. British Mach Vis Conf:1155–1164

  43. Rhemann C, Rother C, Wang J, Gelautz M, Kohli P, Rott P (2009) A perceptually motivated online benchmark for image matting. IEEE Conf Comput Vis Pattern Recogn:1826–1833

  44. Sengupta S, Jayaram V, Curless B, Seitz SM, KemelmacherShlizerman I (2020) Background matting: The world is your green screen. IEEE Conf Comput Vis Pattern Recogn:2291–2300

  45. Shahrian E, Rajan D (2012) Weighted color and texture sample selection for image matting. IEEE Conf Comput Vis Pattern Recogn:718–725

  46. Shahrian E, Rajan D, Price B, Cohen S (2013) Improving image matting using comprehensive sampling sets. IEEE Conf Comput Vis Pattern Recogn:636–643

  47. Shen X, Tao X, Gao H, Zhou C, Jia J (2016) Deep automatic portrait matting. Eur Conf Comput Vis:92–107

  48. Singh S, Jalal AS (2014) Automatic generation of trimap for image matting. Int J Mach Intell Sens Signal Process 1(3):232–250

    Google Scholar 

  49. Sudharshan P, Petitjean C, Spanhol F, Oliveira LE, Heutte L, Honeine P (2019) Multiple instance learning for histopathological breast cancer image classification. Expert Syst Appl 117:103111

  50. Sun J, Jia J, Tang CK, Shum HY (2004) Poisson matting. ACM Trans Graph 23(3):315–321

    Article  Google Scholar 

  51. Tang J, Aksoy Y, Oztireli C, Gross M, Aydin TO (2019) Learningbased sampling for natural image matting. IEEE Conf Comput Vis Pattern Recogn:3055–3063

  52. Tang H, Huang Y, Fan Y, Zeng X (2019) Very deep residual network for image matting. IEEE Int Conf Image Process:4255–4259

  53. Vezhnevets A, Buhmann JM (2010) Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning. IEEE Conf Comput Vis Pattern Recogn:3249–3256

  54. Villar P, Montes R, Sanchez AM, Herrera F (2016) Fuzzy-citationKNN: a fuzzy nearest neighbor approach for multi-instance classification. IEEE Int Conf Fuzzy Syst:946–952

  55. Wang J, Cohen MF (2005) An iterative optimization approach for unified image segmentation and matting. IEEE Int Conf Comput Vis:936–943

  56. Wang J, Cohen MF (2007) Optimized color sampling for robust matting. IEEE Conf Comput Vis Pattern Recogn:1–8

  57. Wang J, Cohen MF (2008) Image and video matting: a survey. Found Trends Comput Graph Vis 3(2):97–175

    Article  Google Scholar 

  58. Wang J, Zucker JD (2000) Solving the Multiple-Instance problem: a lazy learning approach. Seventeenth Int Conf Mach Learn:1119–1125

  59. Wei XS, Wu J, Zhou ZH (2014) Scalable Multi-instance Learning. Int Conf Data Min:1037–1042

  60. Xu N, Price B, Cohen S, Huang TS (2017) Deep Image Matting. arXiv:1703.03872

  61. Xu C, Qi S, Feng J, Xia S, Kang Y, Yao Y, Qian W (2020) DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images. Phys Med Biol 65(14):145011

  62. Yang S, Shen X (2018) Falcon: a fast drop-in replacement of citation knn for multiple instance learning. Int Conf Inf Knowl Manag:67–76

  63. Yao G, Su X, Xin H, Sun J (2021) Adaptively and spatially constrained dual-level trimap generation from sparse inputs. Inf Sci 580:720–733

    Article  MathSciNet  Google Scholar 

  64. Zhang Y, Gong L, Fan L, Ren P, Huang Q, Bao H, Xu W (2019) A late fusion cnn for digital matting. IEEE Conf Comput Vis Pattern Recogn:7469–7478

  65. Zheng Y, Kambhamettu C (2009) Learning based digital matting. IEEE Conf Comput Vis Pattern Recogn:889–896

  66. Zhou W, Alan Conrad B, Hamid Rahim S, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  67. Zhou F, Tian Y, Qi Z (2020) Attention transfer network for nature image matting. IEEE Trans Circ Syst Video Technol 31(6):2192–2205

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Network Center (Information Construction Office), Sanming University, 25 Jingdong Road, Sanming, 365001, Fujian, China

    Zenglu Li

  2. School of Resources and Chemical Engineering, Sanming University, 25 Jingdong Road, Sanming, 365001, Fujian, China

    Xiaoyu Guo

  3. Geography and Ecological Environment Research Center, Fuzhou University, 2 Gakyuan Road, Fuzhou, 350000, Fujian, China

    Songyang Xiang

Authors
  1. Zenglu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Songyang Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyu Guo.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Xiaoyu Guo and Songyang Xiang contributed equally to this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Guo, X. & Xiang, S. Robust trimap optimization algorithm based on Superpixel Citation-KNN. Multimed Tools Appl 81, 33483–33511 (2022). https://doi.org/10.1007/s11042-022-12469-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12469-z

Keywords

Search

Navigation