A brief review: Super-pixel based image segmentation methods

  • Jyotsana Mehra Punjabi University
  • Nirvair Neeru Punjabi University


In this paper image segmentation techniques have been explored which uses super pixel as intermediate step along with fuzzy clustering methods. Superpixel segmentation is the process of partitioning an image into multiple segments called superpixels, which are homogeneous as in pixels inside every portion are comparable concerning certain attributes, for example, shading and surface. In spite of the fact that superpixelsegmentation as a rule yields over-sectioned results instead of item level fragments, it radically diminishes the quantity of picture primitives with insignificant loss of data and offers a simple approach to separate the probably picture objects with as few portions as could be expected under the circumstances. Likewise, since superpixelsegmentation gives a more characteristic and perceptually significant representation of the info picture, it is more helpful and powerful to concentrate area based visual elements utilizing superpixels. In order to get better segmentation the FCM use the GLCM features of turbo pixels instead of intensity values of pixels and hence help in decision making to put a particular turbo-pixel  into different fcm clusters. In the proposed work, we will explore these techniques in order to get better segmentation of different sections of the input images.


Download data is not yet available.


[1] N. Pal, S. Pal, A review on image segmentation techniques, Pattern Recogn. 26
(1993) 1277–1294.

[2] I. Despotovic, E. Vansteenkiste, W. Philips, Spatially coherent fuzzy clustering
for accurate and noise-robust image segmentation, IEEE Signal Process. Lett.
20 (4) (2013) 295–298.

[3] X. Li, L. Li, H. Lu, D. Chen, Z. Liang, Inhomogeneity correction for magnetic resonance images with fuzzy c-mean method, in: Proc. SPIE Int. Soc. Opt. Eng., vol. 5032, 2003, pp. 995–1005.

[4] X. Ren, J. Malik, Learning a classification model for segmentation, in: Int. Conf.Computer Vision, vol. 1, 2003, pp. 10–17.

[5]XiaolinTian , Licheng Jiao, Long Yi, KaiwuGuo, Xiaohua Zhang ,” The image segmentation based on optimized spatial feature of superpixel,” published in J. Vis. Commun. Image R. 26 (2015) 146–160

[7] X. Ren and J. Malik.Learning a classi_cation model for segmentation.In Computer Vision, pages 10{17, 2003.

[8] J. Shotton, M. Johnson, R. Cipolla, Semantic texton forests for image categorization and segmentation, in: European Conference on Computer Vision, 2008, pp. 1–8.

[9]ManishaVerma ,Balasubramanian Raman, “Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval” published in J. Vis. Commun. Image R R. 32 (2015) 224–236.

[10] C Rohkohl; K. Engel; “Efficient image segmentation using pairwise pixel similarities.”In Pattern Recognition, pages 254-63, 2007.

[11]Tiange Liu a, Qiguang Miao, KuanTian, Jianfeng Song, Yun Yang, Yutao Qi, “SCTMS: Superpixel based color topographic map segmentation method” Published in Journal of Visual Communication and Image Representation Volume 35, February 2016, Pages 78–90.

[12] Chang hang Xu, Jing Xie, Guoming Chen, Weiping Huang, “An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface” Published in Infrared Physics & Technology Volume 67, November 2014, Pages 266–272.

[13] P. Buyssensa, I. Gardina, S. Ruan, “Eikonal based region growing for superpixels generation: Application tosemi-supervised real time organ segmentation in CT images” Published in IRBM Volume 35, Issue 1, February 2014, Pages 20–26.

[14] H. EmrahTasli, Ronan Sicre, Theo Gevers, “Super Pixel based mid-level image description for image recognition” Published in Pattern Recognition (ICPR), 2014 22nd International Conference on Date of Conference: 24-28 Aug. 2014 Page(s): 3732 – 3737.

[15] Yong Yang, Ling Guo, TianjiangWang ,Wenbing Tao, Guangpu Shao, Qi Feng, “Unsupervised multiphase color–texture image segmentation based on variational formulation and multilayer graph” Published in Image and Vision Computing Volume 32, Issue 2, February 2014, Pages 87–106.

[16]NikolaosDimitriou, AnastasiosDelopoulos, “Incorporating higher order models for occlusion resilient motion segmentation in streaming videos” Published in Image and Vision Computing Volume 36, April 2015, Pages 70–82.

[17] Xiang-Yang Wang, Zhi-Fang Wu, Liang Chen, Hong-Liang Zheng, Hong-Ying Yang, “Pixel classification based color image segmentation using quaternion exponent moments” Published in Neural Networks : the Official Journal of the International Neural Network Society 2016, 74:1-13.

[18] Xiao linTian, Licheng Jiao, Long Yi, KaiwuGuo, Xiaohua Zhang, “The image segmentation based on optimized spatial feature of superpixel” Published in Journal of Visual Communication and Image Representation Volume 26, January 2015, Pages
How to Cite
MEHRA, Jyotsana; NEERU, Nirvair. A brief review: Super-pixel based image segmentation methods. International Journal of Research and Engineering, [S.l.], v. 4, n. 7, p. 200-205, sep. 2017. ISSN 2348-7860. Available at: <https://digital.ijre.org/index.php/int_j_res_eng/article/view/182>. Date accessed: 15 sep. 2019.


Super-pixels, Segmentation, FCM,DWT, GLCM