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Interactive Image Segmentation with Superpixel Propagation

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Abstract

Interactive tools play an important role in solving image segmentation problems. In this paper, we present a new interactive segmentation framework for high-accuracy segmentation. The main interaction of the user is to provide clicks inside the object of interest and control the mask-growing process with a slider. We use propagation on superpixels for region growth. To do large-scale evaluation we automate the user interactions and compare our method with state-of-the-art approaches on a few datasets with detailed annotations. Our method consistently outperforms the competitors on high accuracies and certain classes of images. We also do experiments with human annotators to show how more time-consuming naive approaches are compared to our method. Moreover, in contrast to state-of-the-art deep learning methods that stop improving segmentation accuracy beyond 20–100 clicks, our algorithm guarantees accuracy improvement after every iteration.

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References

  1. OpenCV library.https://opencv.org/. Accessed 28 Aug 2023

  2. Benenson, R., Popov, S., Ferrari, V.: Large-scale interactive object segmentation with human annotators. In: CVPR, pp. 11700–11709 (2019)

    Google Scholar 

  3. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell.40(4), 834–848 (2017)

    Article  Google Scholar 

  4. Chen, X., Zhao, Z., Yu, F., Zhang, Y., Duan, M.: Conditional diffusion for interactive segmentation. In: ICCV, pp. 7345–7354 (2021)

    Google Scholar 

  5. Chen, X., Zhao, Z., Zhang, Y., Duan, M., Qi, D., Zhao, H.: Focalclick: towards practical interactive image segmentation. In: CVPR, pp. 1300–1309 (2022)

    Google Scholar 

  6. Cheng, B., Girshick, R., Dollár, P., Berg, A.C., Kirillov, A.: Boundary iou: Improving object-centric image segmentation evaluation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15334–15342 (2021)

    Google Scholar 

  7. Galeev, D., Popenova, P., Vorontsova, A., Konushin, A.: Contour-based interactive segmentation. arXiv preprintarXiv:2302.06353 (2023)

  8. Gupta, A., Dollar, P., Girshick, R.: LVIS: a dataset for large vocabulary instance segmentation. In: CVPR (2019)

    Google Scholar 

  9. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV, pp. 2961–2969 (2017)

    Google Scholar 

  10. Jang, W.D., Kim, C.S.: Interactive image segmentation via backpropagating refinement scheme. In: CVPR, pp. 5297–5306 (2019)

    Google Scholar 

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

    Article  Google Scholar 

  12. Lin, T.Y., et al.: Microsoft COCO: Common objects in context. In: ECCV, pp. 740–755 (2014)

    Google Scholar 

  13. Lin, Z., Duan, Z.P., Zhang, Z., Guo, C.L., Cheng, M.M.: Focuscut: diving into a focus view in interactive segmentation. In: CVPR, pp. 2637–2646 (2022)

    Google Scholar 

  14. Lin, Z., Zhang, Z., Chen, L.Z., Cheng, M.M., Lu, S.P.: Interactive image segmentation with first click attention. In: CVPR, pp. 13339–13348 (2020)

    Google Scholar 

  15. Liu, Q., et al.: Pseudoclick: interactive image segmentation with click imitation. In: ECCV, pp. 728–745 (2022)

    Google Scholar 

  16. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)

    Google Scholar 

  17. Malladi, R., Sethian, J.A.: An O(N log N) algorithm for shape modeling. Proc. National Acad. Sci.93(18), 9389–9392 (1996)

    Article MathSciNet  Google Scholar 

  18. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, pp. 416–423 (2001)

    Google Scholar 

  19. McGuinness, K., O’Connor, N.E.: A comparative evaluation of interactive segmentation algorithms. Pattern Recogn.43(2), 434–444 (2010)

    Article  Google Scholar 

  20. Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR, pp. 724–732 (2016)

    Google Scholar 

  21. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788 (2016)

    Google Scholar 

  22. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI, pp. 234–241 (2015)

    Google Scholar 

  23. Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph.23(3), 309–314 (2004)

    Google Scholar 

  24. Sethian, J.A.: Fast marching methods. SIAM Rev.41(2), 199–235 (1999)

    Article MathSciNet  Google Scholar 

  25. Sethian, J.A.: Evolution, implementation, and application of level set and fast marching methods for advancing fronts. J. Comput. Phys.169(2), 503–555 (2001)

    Article MathSciNet  Google Scholar 

  26. Sharma, G., Wu, W., Dalal, E.N.: The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color. Res. Appl.30(1), 21–30 (2005)

    Article  Google Scholar 

  27. Sofiiuk, K., Petrov, I., Barinova, O., Konushin, A.: F-brs: rethinking backpropagating refinement for interactive segmentation. In: CVPR, pp. 8623–8632 (2020)

    Google Scholar 

  28. Sofiiuk, K., Petrov, I.A., Konushin, A.: Reviving iterative training with mask guidance for interactive segmentation. arXiv preprintarXiv:2102.06583 (2021)

  29. Sofiiuk, K., Petrov, I.A., Konushin, A.: Reviving iterative training with mask guidance for interactive segmentation. In: ICIP, pp. 3141–3145 (2022)

    Google Scholar 

  30. Stutz, D., Hermans, A., Leibe, B.: Superpixels: an evaluation of the state-of-the-art. Comput. Vis. Image Underst.166, 1–27 (2018)

    Article  Google Scholar 

  31. Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L.: Solo: Segmenting objects by locations. In: ECCV, pp. 649–665 (2020)

    Google Scholar 

  32. Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: Denseaspp for semantic segmentation in street scenes. In: CVPR, pp. 3684–3692 (2018)

    Google Scholar 

  33. Yao, J., Boben, M., Fidler, S., Urtasun, R.: Real-time coarse-to-fine topologically preserving segmentation. In: CVPR, pp. 2947–2955 (2015)

    Google Scholar 

  34. Yeghiazaryan, V.: Parallel Front Propagation in Medical Image Segmentation. DPhil thesis, University of Oxford (2018)

    Google Scholar 

  35. Yeghiazaryan, V., Voiculescu, I.: Family of boundary overlap metrics for the evaluation of medical image segmentation. J. Med. Imag.5(1), 015006–015006 (2018)

    Article  Google Scholar 

  36. Zhang, S., Liew, J.H., Wei, Y., Wei, S., Zhao, Y.: Interactive object segmentation with inside-outside guidance. In: CVPR, pp. 12234–12244 (2020)

    Google Scholar 

  37. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: CVPR, pp. 633–641 (2017)

    Google Scholar 

  38. Zhou, M., et al.: Interactive segmentation as gaussion process classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19488–19497 (2023)

    Google Scholar 

Download references

Acknowledgements

Varduhi Yeghiazaryan’s participation in this project was supported by Picsart AI Research. We also thank the annotators for their time and contribution to our research.

Author information

Authors and Affiliations

  1. Picsart AI Research (PAIR), Yerevan, Armenia

    Hrach Ayunts & Shant Navasardyan

  2. Picsart AI Research (PAIR), Miami, USA

    Humphrey Shi

  3. American University of Armenia, Yerevan, Armenia

    Varduhi Yeghiazaryan

  4. University of Oregon, Eugene, USA

    Humphrey Shi

  5. University of Illinois Urbana-Champaign, Champaign, USA

    Humphrey Shi

  6. Georgia Institute of Technology, Atlanta, USA

    Humphrey Shi

Authors
  1. Hrach Ayunts

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  2. Varduhi Yeghiazaryan

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  3. Shant Navasardyan

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  4. Humphrey Shi

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Corresponding author

Correspondence toVarduhi Yeghiazaryan.

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  3. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  4. American University of Armenia, Yerevan, Armenia

    Habet Madoyan

  5. Artificial Intelligence Research Institute, Moscow, Russia

    Ilya Makarov

  6. Universität Hamburg, Hamburg, Germany

    Irina Nikishina

  7. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  8. Mohamed bin Zayed University of Artificial Intelligence and Technology Innovation Institute, Abu Dhabi, United Arab Emirates

    Maxim Panov

  9. Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  10. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  11. Apptek, Aachen, Nordrhein-Westfalen, Germany

    Evgenii Tsymbalov

  12. Kazan Federal University and HSE University, Moscow, Russia

    Elena Tutubalina

  13. MTS AI, Moscow, Russia

    Sergey Zagoruyko

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Ayunts, H., Yeghiazaryan, V., Navasardyan, S., Shi, H. (2024). Interactive Image Segmentation with Superpixel Propagation. In: Ignatov, D.I.,et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2023. Communications in Computer and Information Science, vol 1905. Springer, Cham. https://doi.org/10.1007/978-3-031-67008-4_13

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