ACCELERATED ALGORITHMS FOR GROWING SEGMENTS FROMIMAGE REGIONS

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Abstract

The paper proposes new algorithms for combining superpixel regions into segments. The main idea is that when combining super pixels, firstly, a strategy is used in which the segment is grown from neighboring areas as long as the conditions for combining are met, and secondly, when combining areas, the applied information quality measure should not increase. Three algorithms based on this strategy are proposed, which differ in the conditions for making a decision on combining superpixels. A computational experiment was carried out on test images. The experiment showed that the proposed algorithms make it possible to speed up the segmentation process compared to the procedure used, with acceptable losses of information quality measures of the obtained partitions.

About the authors

D. M. Murashov

FRC CSC RAS

Email: d_murashov@mail.ru
Moscow

References

  1. Ikonomatakis N., Plataniotis K. N., Zervakis M., Venetsanopoulos A. N. Region growing and region merging image segmentation // Proc. of 13th Internat. Conference on Digital Signal Proc., Santorini, Greece. 1997. Vol. 1. P. 299–302. doi: 10.1109/ICDSP.1997.628077.
  2. Peng B., Zhang L., Zhang D. Automatic Image Segmentation by Dynamic Region Merging // IEEE Transactions on Image Proc.. 2011. Vol. 20(12). P. 3592–3605. doi: 10.1109/TIP.2011.2157512.
  3. Ko H.Y., Ding J.J. Adaptive growing and merging algorithm for image segmentation // 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea (South). 2016. P. 1–10. doi: 10.1109/APSIPA.2016.7820762.
  4. Chamalis T., Likas A. Region merging for image segmentation based on unimodality tests // 2017 3rd Internat. Conference on Control, Automation and Robotics (ICCAR), Nagoya, Japan. 2017. P. 381–384. doi: 10.1109/ICCAR.2017.7942722.
  5. Tang R., Song W., Guan X., Ge H., Kong D. Dam Burst: A region-merging-based image segmentation method // ArXiv. 2020. abs/2003.04797. https://doi.org/10.48550/arXiv.2003.04797.
  6. Kharinov M. Stable Segmentation of Digital Image //arXiv preprint arXiv:1208.2655. 2012.
  7. Shuanhu Di, Miao Liao, Yuqian Zhao, Yang Li, Yezhan Zeng. Image superpixel segmentation based on hierarchical multi-level LI-SLIC // Optics & Laser Technology. 2021. Vol. 135. 106703. https://doi.org/10.1016/j.optlastec.2020.106703.
  8. Liu T., Seyedhosseini M., Tasdizen T. Image Segmentation Using Hierarchical Merge Tree // IEEE Transactions on Image Proc. 2016. Vol. 25(10). P. 4596–4607. doi: 10.1109/TIP.2016.2592704.
  9. Чочиа П.А. Сегментация изображений на основе анализа расстояний в пространстве признаков // Автометрия. 2014. №4. С. 97–110.
  10. Альмияхи О.М., Цветков В.Ю., Конопелько В.К., Гусева О.В. Адаптивное двухпороговое квантование и сегментация изображений на основе разделения и слияния областей // Докл. БГУИР. 2016. №7(101). С. 183–187.
  11. Calderero F., Marques F. Region Merging Techniques Using Information Theory Statistical Measures // IEEE Transactions on Image Proc. 2010. Vol. 19(6). P. 1567–1586. doi: 10.1109/TIP.2010.2043008.
  12. Rigau J., Feixas M., Sbert S. An information theoretic framework for image segmentation // 2004 Internat. Conference on Image Proc. ICIP ’04. Singapore. 2004. Vol. 2. P. 1193–1196. doi: 10.1109/ICIP.2004.1419518.
  13. Bardera A., Rigau J., Boada I., Feixas M., Sbert S. Image Segmentation Using Information Bottleneck Method // IEEE Transactions on Image Processing. 2009. Vol. 18(7). P. 1601–1612. doi: 10.1109/TIP.2009.2017823.
  14. Mirsadeghi S. E., Royat A., Rezatofighi H. Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization // IEEE Robotics and Automation Letters. 2021. Vol. 6(4). P. 6931–6938. doi: 10.1109/LRA.2021.3095311.
  15. Tishby N. Pereira F. C., Bialek W. The information bottleneck method // Proc. of the 37th Annual Allerton Conference on Communication, Control and Computing. 1999. P. 368–377.
  16. Lv X., Persello C., Huang X., Ming D., Stein A. DeepMerge: Deep Learning-Based Region-Merging for Image Segmentation //arXiv preprint arXiv:2305.19787v2[cs.CV]. – 2024. https://doi.org/10.48550/arXiv.2305.19787
  17. Murashov D. Theoretical-information quality model for image segmentation // Procedia Engng. 2017. Vol. 201. P. 239–248.
  18. Murashov D. Application of Information Redundancy Measure To Image Segmentation. In: Strijov V., Ignatov D., Vorontsov K. (eds) Intelligent Data Processing. IDP 2016. Communications in Computer and Information Science. Springer, Cham. 2019. Vol. 794. P. 125–139. DOI: https://doi.org/10.1007/978-3-030-35400-8_9
  19. Murashov D.M. An Information Model for Digital Image Segmentation // Pattern Recognit. Image Anal. 2021. Vol. 31. P. 632–645.
  20. Achanta R., Shaji A., Smith K., Lucchi A., Fua P., Susstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods // IEEE Trans. Pattern Anal. Mach. Intell. 2012. Vol. 34(11). P. 2274–2282.
  21. Ланге А.М., Ланге М.М., Парамонов С.В. О соотношении взаимной информации и вероятности ошибки в задаче классификации данных // Ж. вычисл. матем. и матем. физ. 2021. T. 61.№7. С. 1192–1205.
  22. Cover T.M, Thomas J.A. Elements of Information Theory.Wiley Series in Telecommunications. 2-nd ed. JohnWiley & Sons, Inc. 2006. 774 P.
  23. Meila M. Comparing clusterings: an axiomatic view // In: Proc. of the 22nd Int. Conf. on “Machine learning (ACM, 2005)”, Bonn, Germany, August 7 – 11 2005. ACM, New York, NY, USA. 2005. P. 577–584.
  24. Arbelaez P., Maire M., Fowlkes C., Malik J. Contour Detection and Hierarchical Image Segmentation // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011. Vol. 33(5), P. 898–916. doi: 10.1109/TPAMI.2010.161
  25. Martin D., Fowlkes C.,Tal D., Malik J.Adatabase of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics // Proc. “8th Int. Conf. on Comput. Vis. (ICCV 2001)”, Vancouver, BC, Canada, 07–14 July 2001. IEEE. 2001. Vol. 2. P. 416–423.

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