LONG Peng, LU Huaxiang, WANG An, “A Novel Unsupervised Two-Stage Technique in Color Image Segmentation,” Chinese Journal of Electronics, vol. 27, no. 2, pp. 405-412, 2018, doi: 10.1049/cje.2018.01.011
Citation: LONG Peng, LU Huaxiang, WANG An, “A Novel Unsupervised Two-Stage Technique in Color Image Segmentation,” Chinese Journal of Electronics, vol. 27, no. 2, pp. 405-412, 2018, doi: 10.1049/cje.2018.01.011

A Novel Unsupervised Two-Stage Technique in Color Image Segmentation

doi: 10.1049/cje.2018.01.011
Funds:  This work is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDB02080002), and the National Natural Foundation of China for Young Scientist (No.61401423).
More Information
  • Corresponding author: LONG Peng (corresponding author) received the B.S. degree in Electronic of Science and Technology from Huazhong University of Science and Technology in 2012, and is currently working toward the Ph.D. degree in the Institute of Semiconductors, Chinese Academy of Sciences (CAS). His current research interests include the image processing and pattern recognition. (Email:longpeng@semi.ac.cn)
  • Received Date: 2015-03-09
  • Rev Recd Date: 2016-01-19
  • Publish Date: 2018-03-10
  • A new unsupervised two-stage method for color image segmentation is proposed. The method contains coarse segmentation and delicate segmentation. In coarse segmentation, we adaptively choose a gray channel from CIE-lab color space. The Otsu method combined with a refinement to its threshold is applied to get global optimal segmentation. In delicate segmentation, a narrowband based procedure is applied to get more accurate contour of the object and local optimal segmentation is achieved. Our method finally balance the global optimal and the local optimal. The proposed method does not need initial contours or initial labels, thus it is more robust in certain applications. Experimental results of our method in MSRA1000 database show that our method is robust in segmenting objects and backgrounds when possessing weakly heterogeneous color. Our method firstly achieves global optimal and then achieves local optimal which draws a new and prospective outlook for segmenting color images.
  • loading
  • N. Senthilkumaran and R. Rajesh, "Edge detection techniques for image segmentation-A survey of soft computing approaches", International Journal of Recent Trends in Engineering, Vol.1, No.2, pp.250-254, 2009.
    M. Sezgin, "Survey over image thresholding techniques and quantitative performance evaluation", Journal of Electronic Imaging, Vol.13, No.1, pp.146-168, 2004.
    Q. Hu, Z. Hou and W.L. Nowinski, "Supervised rangeconstrained thresholding", Image Processing, Vol.15, No.1, pp.228-240, 2006.
    J.L. Fan and B. Lei, "A modified valley-emphasis method for automatic thresholding", Pattern Recognition Letters, Vol.33, No.6, pp.703-708, 2012.
    B. Peng and D. Zhang, "Automatic image segmentation by dynamic region merging", Image Processing, Vol.20, No.12, pp.3592-3605, 2011.
    F.Y. Shih and S. Cheng, "Automatic seeded region growing for color image segmentation", Image and Vision Computing, Vol.23, No.10, pp.877-886, 2005.
    V. Grau, A.U.J. Mewes and M. Alcaniz, "Improved watershed transform for medical image segmentation using prior information", Medical Imaging, Vol.23, No.4, pp.447-458, 2004.
    T. Kanungo, D.M. Mount and N.S. Netanyahu, "An efficient k-means clustering algorithm:Analysis and implementation", Pattern Analysis and Machine Intelligence, Vol.24, No.7, pp.881-892, 2002.
    M. Kass, A. Witkin and D. Terzopoulos,"Snakes:Active contour models", International Journal of Computer Vision, Vol.1, No.4, pp.321-331, 1988.
    C. Li, C.Y. Kao and J.C. Gore, "Minimization of regionscalable fitting energy for image segmentation", Image Processing, Vol.17, No.10, pp.1940-1949, 2008.
    A.Y. Yang, I. Wright, Y. Ma, et al., "Unsupervised segmentation of natural images via lossy data compression", Computer Vision and Image Understanding, Vol.110, No.2, pp.212-225, 2008.
    P.F. Felzenszwal and D.P. Huttenlocher, "Efficient graph-based image segmentation", International Journal of Computer Vision, Vol.59, No.2, pp.167-181, 2004.
    Q. Shi, L. Zhang and B. Du, "Semisupervised discriminative locally enhanced alignment for hyperspectral image classification", IEEE Transactions on Geoscience and Remote Sensing, Vol.51, No.9, pp.4800-4815, 2013.
    B. Du and L. Zhang, "A discriminative metric learning based anomaly detection method", IEEE Transactions on Geoscience and Remote Sensing, Vol.52, No.11, pp.6844-6857, 2014.
    B. Du and L. Zhang, "Target detection based on a dynamic subspace", Pattern Recognition, Vol.47, No.1, pp.344-358, 2014.
    D. Adalsteinsson and J.A. Sethian, "A fast level set method for propagating interfaces", Journal of Computational Physics, Vol.118, No.2, pp.269-277, 1995.
    H. Lombaert, Y. Sun, L. Grady, et al., "A multilevel banded graph cuts method for fast image segmentation", IEEE International Conference on Computer Vision, Vol.1, pp.259-265, 2005.
    W.M. Rand, "Objective criteria for the evaluation of clustering methods", Journal of the American Statistical association, Vol.66, No.336, pp.846-850, 1971.
    M. Meilǎ, "Comparing clusterings-An information based distance", Journal of Multivariate Analysis, Vol.98, No.5, pp.873-895, 2007.
    D. Martin, C. Fowlkes, D. Tal, et al., "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics", IEEE International Conference on Computer Vision, Vol.2, pp.416-423, 2001.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (675) PDF downloads(306) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return