COMPARATIVE ANALYSIS OF SUPERPIXEL SEGMENTATION METHODS

Authors

  • SumitKaur Research Scholar, Gurukashi University, TalwandiSaboo, India
  • Dr. R.K Bansal Dean Research, Gurukashi University, TalwandiSaboo, India

DOI:

https://doi.org/10.29121/ijetmr.v5.i3.2018.172

Keywords:

Image segmentation, Morphological Processing, Superpixel

Abstract

Superpixel segmentation showed to be a useful preprocessing step in many computer vision applications. Superpixel’s purpose is to reduce the redundancy in the image and increase efficiency from the point of view of the next processing task. This led to a variety of algorithms to compute superpixel segmentations, each with individual strengths and weaknesses. Many methods for the computation of superpixels were already presented. A drawback of most of these methods is their high computational complexity and hence high computational time consumption. K mean based SLIC method shows better performance as compare to other while evaluating on the bases of under segmentation error and boundary recall, etc parameters.

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References

X. Ren and J. Malik, “Learning a classification model for segmentation,” in Proc. IEEE Conf. International Conference on Computer Vision (ICCV), pp. 10-17, 2003. DOI: https://doi.org/10.1109/ICCV.2003.1238308

Pedro Felzenszwalb and Daniel Huttenlocher.Efficient graph-based image segmentation. International Journal of Computer Vision (IJCV), 59(2):167–181, September 2004. DOI: https://doi.org/10.1023/B:VISI.0000022288.19776.77

Jianbo Shi and Jitendra Malik.Normalized cuts and image segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), 22(8):888–905, Aug 2000. DOI: https://doi.org/10.1109/34.868688

Y. Boykov and M. Jolly.Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images.In International Conference on Computer Vision (ICCV), 2001.

A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua. A fully automated approach to segmentation of irregularly shaped cellular structures in em images. International Conference on Medical ImageComputing and Computer Assisted Intervention, 2010. DOI: https://doi.org/10.1007/978-3-642-15745-5_57

S. Gould, J. Rodgers, D. Cohen, G. Elidan, and D. Koller, “Multiclass segmentation with relative location prior,” IJCV, 2008. DOI: https://doi.org/10.1007/s11263-008-0140-x

J. M. Gonfaus, X. Boix, J. Van de Weijer, A. D. Bagdanov, J. Serrat, and J. Gonzalez, “Harmony potentials for joint classification and segmentation,” in CVPR, 2010 DOI: https://doi.org/10.1109/CVPR.2010.5540048

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slicsuperpixels compared to state-of-the-art superpixel methods,” TPAMI, 2012. DOI: https://doi.org/10.1109/TPAMI.2012.120

S. Gould, J. Rodgers, D. Cohen, G. Elidan, and D. Koller, “Multiclass segmentation with relative location prior,” IJCV, 2008. DOI: https://doi.org/10.1007/s11263-008-0140-x

J. Shotton, J. Winn, C. Rother, and A. Criminisi, “Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation,” in ECCV, 2006. DOI: https://doi.org/10.1007/11744023_1

T. Malisiewicz and A. A. Efros, “Improving spatial support for objects via multiple segmentations,” in BMVC, 2007 DOI: https://doi.org/10.5244/C.21.55

Y. Qin, H. Lu, Y. Xu, and H. Wang, “Saliency detection via cellular automata,” in CVPR, 2015 DOI: https://doi.org/10.1109/CVPR.2015.7298606

Q. Yang, “A non-local cost aggregation method for stereo matching,” in CVPR, 2012

X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang, “Segment-tree based cost aggregation for stereo matching,” in CVPR, 201

J. Shi, J. Malik. Normalized cuts and image segmentation. Transactions on Pattern Analysis and Machine Intelligence, 22(8):888–905, 2000 DOI: https://doi.org/10.1109/34.868688

X. Ren, J. Malik. Learning a classification model for segmentation. International Conference on Computer Vision, pages 10–17, 2003. DOI: https://doi.org/10.1109/ICCV.2003.1238308

Alastair Moore, Simon Prince, Jonathan Warrell, Umar Mohammed, and Graham Jones.Superpixel Lattices.IEEE Computer Vision and PatternRecognition (CVPR), 2008. DOI: https://doi.org/10.1109/CVPR.2008.4587471

ShaiAvidan and Ariel Shamir. Seam carving for content-aware image resizing. ACM Transactions on Graphics (SIGGRAPH), 26(3), 2007 DOI: https://doi.org/10.1145/1276377.1276390

A. Vedaldi, S. Soatto. Quick shift and kernel methods for mode seeking. European Conference on Computer Vision, pages 705–718, 2008. DOI: https://doi.org/10.1007/978-3-540-88693-8_52

A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Siddiqi. TurboPixels: Fast superpixels using geometric flows. Transactions on Pattern Analysis and Machine Intelligence, 31(12):2290–2297, 2009

Luc Vincent and Pierre Soille. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions onPattern Analalysis and Machine Intelligence, 13(6):583–598, 1991.

D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and MachineIntelligence, 24(5):603–619, May 2002 DOI: https://doi.org/10.1109/34.1000236

J. Shen, Y. Du, W. Wang, and X. Li, “Lazy random walks for superpixel segmentation,” IEEE Trans. Image Process., vol. 23, no. 4, pp. 1451–1462, Apr. 2014 DOI: https://doi.org/10.1109/TIP.2014.2302892

O. Veksler, Y. Boykov, and P. Mehrani, “Superpixels and supervoxels in an energy optimization framework,” in Proc. ECCV, 2010, pp. 211–224. DOI: https://doi.org/10.1007/978-3-642-15555-0_16

D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc. IEEE Int. Conf.Comput. Vis., vol. 2. Jul. 2001, pp. 416–423.

Achanta, Radhakrishna, et al. "SLIC superpixels compared to state-of-the-art superpixel methods." IEEE transactions on pattern analysis and machine intelligence 34.11 (2012): 2274- 2282. DOI: https://doi.org/10.1109/TPAMI.2012.120

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Published

2018-03-31

How to Cite

Kaur, S., & Bansal, R. (2018). COMPARATIVE ANALYSIS OF SUPERPIXEL SEGMENTATION METHODS . International Journal of Engineering Technologies and Management Research, 5(3), 1–9. https://doi.org/10.29121/ijetmr.v5.i3.2018.172