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Improvements on the Superpixel Hierarchy Algorithm with Applications to Image Segmentation and Saliency Detection

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12509))

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Abstract

Superpixel techniques aim to divide an image into predefined number of regions or groups of pixels, to facilitate operations such as segmentation. However, finding the optimal number of regions for each image becomes a difficult task due to the large difference of features observed in images. However, with the help of edge and color information, we can target an ideal number of regions for each image. This work presents two modifications to the known Superpixel hierarchy algorithm. These changes aim to define the number of superpixels automatically through edge information with different orientations and the Hue channel of the HSV color model. The results are presented quantitatively and qualitatively for edge detection and saliency estimation problems. The experiments were conducted on the BSDS500 and ECSSD datasets.

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References

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

    Article  Google Scholar 

  2. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011). https://doi.org/10.1109/TPAMI.2010.161

    Article  Google Scholar 

  3. Aytekin, C., Kiranyaz, S., Gabbouj, M.: Automatic object segmentation by quantum cuts. In: 2014 22nd International Conference on Pattern Recognition, pp. 112–117 (2014)

    Google Scholar 

  4. Aytekin, C., Ozan, E.C., Kiranyaz, S., Gabbouj, M.: Visual saliency by extended quantum cuts. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 1692–1696 (2015)

    Google Scholar 

  5. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017)

    Google Scholar 

  6. Dollár, P., Zitnick, C.L.: Structured forests for fast edge detection. In: 2013 IEEE International Conference on Computer Vision, pp. 1841–1848 (2013)

    Google Scholar 

  7. Fang, B., Zhang, X., Ma, Y., Han, Y.: DTI images segmentation based on adaptive bandwidth mean shift algorithm. In: 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 248–251 (2016)

    Google Scholar 

  8. Fang, F., Wang, T., Zeng, T., Zhang, G.: A superpixel-based variational model for image colorization. IEEE Trans. Visualization Comput. Graph. 1 (2019)

    Google Scholar 

  9. Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975)

    Article  MathSciNet  Google Scholar 

  10. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org

  11. Li, H., Lu, H., Lin, Z., Shen, X., Price, B.: Inner and inter label propagation: salient object detection in the wild. IEEE Trans. Image Process. 24(10), 3176–3186 (2015)

    Article  MathSciNet  Google Scholar 

  12. Li, H., Lu, H., Lin, Z., Shen, X., Price, B.: Inner and inter label propagation: salient object detection in the wild (2015)

    Google Scholar 

  13. Maire, M., Arbelaez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  14. Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2014)

    Google Scholar 

  15. Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)

    Article  Google Scholar 

  16. Nameirakpam, D., Singh, K., Chanu, Y.: Image segmentation using k -means clustering algorithm and subtractive clustering algorithm. Procedia Comput. Sci. 54, 764–771 (2015). https://doi.org/10.1016/j.procs.2015.06.090

    Article  Google Scholar 

  17. Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)

    Article  Google Scholar 

  18. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  19. Reinhard, E., Khan, E.A., Akyz, A.O., Johnson, G.M.: Color Imaging: Fundamentals and Applications. A. K. Peters, Ltd., Wellesley (2008)

    Google Scholar 

  20. Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 10–17 (2003)

    Google Scholar 

  21. Wang, Y., Ding, W., Zhang, B., Li, H., Liu, S.: Superpixel labeling priors and MRF for aerial video segmentation. IEEE Trans. Circ. Syst. Video Technol. 1 (2019)

    Google Scholar 

  22. Wei, X., Yang, Q., Gong, Y., Ahuja, N., Yang, M.: Superpixel hierarchy. IEEE Trans. Image Process. 27(10), 4838–4849 (2018)

    Article  MathSciNet  Google Scholar 

  23. Xie, S., Tu, Z.: Holistically-nested edge detection. In: IEEE International Conference on Computer Vision (ICCV), pp. 1395-1403 (2015)

    Google Scholar 

  24. Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162 (2013)

    Google Scholar 

  25. Yang, H., Huang, C., Wang, F., Song, K., Yin, Z.: Robust semantic template matching using a superpixel region binary descriptor. IEEE Trans. Image Process. 28(6), 3061–3074 (2019)

    Article  MathSciNet  Google Scholar 

  26. Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., Mech, R.: Minimum barrier salient object detection at 80 fps. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1404–1412 (2015)

    Google Scholar 

  27. Zhang, Y., Li, X., Gao, X., Zhang, C.: A simple algorithm of superpixel segmentation with boundary constraint. IEEE Trans. Circuits Syst. Video Technol. 27(7), 1502–1514 (2017)

    Google Scholar 

  28. Zheng, Y., Zheng, P.: Hand tracking based on adaptive kernel bandwidth mean shift. In: 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, pp. 548–552 (2016)

    Google Scholar 

  29. Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)

    Google Scholar 

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Authors and Affiliations

  1. Centro de Informética, Universidade Federal de Pernambuco, Recife, Brazil

    Marcos J. C. E. Azevedo & Carlos A. B. Mello

Authors
  1. Marcos J. C. E. Azevedo
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  2. Carlos A. B. Mello
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Corresponding author

Correspondence to Marcos J. C. E. Azevedo .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Azevedo, M.J.C.E., Mello, C.A.B. (2020). Improvements on the Superpixel Hierarchy Algorithm with Applications to Image Segmentation and Saliency Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-64556-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64555-7

  • Online ISBN: 978-3-030-64556-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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