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基于两段多组件图割的非监督彩色图像分割算法

董卓莉 李磊 张德贤

董卓莉, 李磊, 张德贤. 基于两段多组件图割的非监督彩色图像分割算法. 自动化学报, 2014, 40(6): 1223-1232. doi: 10.3724/SP.J.1004.2014.01223
引用本文: 董卓莉, 李磊, 张德贤. 基于两段多组件图割的非监督彩色图像分割算法. 自动化学报, 2014, 40(6): 1223-1232. doi: 10.3724/SP.J.1004.2014.01223
DONG Zhuo-Li, LI Lei, ZHANG De-Xian. Unsupervised Color Image Segmentation Using Two-phase Graph Cuts with Multiple Components. ACTA AUTOMATICA SINICA, 2014, 40(6): 1223-1232. doi: 10.3724/SP.J.1004.2014.01223
Citation: DONG Zhuo-Li, LI Lei, ZHANG De-Xian. Unsupervised Color Image Segmentation Using Two-phase Graph Cuts with Multiple Components. ACTA AUTOMATICA SINICA, 2014, 40(6): 1223-1232. doi: 10.3724/SP.J.1004.2014.01223

基于两段多组件图割的非监督彩色图像分割算法

doi: 10.3724/SP.J.1004.2014.01223
基金项目: 

国家自然科学基金(60972098,70701013),河南省教育厅自然科学研究项目(13A520177)资助

详细信息
    作者简介:

    董卓莉 河南工业大学信息科学与工程学院讲师. 主要研究方向为图像处理、计算机视觉和模式识别.E-mail:dong lily2002@haut.edu.cn

Unsupervised Color Image Segmentation Using Two-phase Graph Cuts with Multiple Components

Funds: 

Supported by National Natural Science Foundation of China (60972098, 70701013), and Natural Science Research Project of Department of Education of Henan Province (13A520177)

  • 摘要: 提出基于两段多组件图割的彩色图像分割算法,以解决因标签过多和噪声导致的过分割和图割算法低效等问题.多组件图割算法分割图像时,把标签相同的区域处理为该标签的多个组件,结合两层高斯金字塔形成两段多组件图割,以减少分割错误和标签数量,提高分割的性能.算法首先提取基于多尺度四元数Gabor滤波的texton纹理特征,并自适应融合颜色特征;然后使用两段多组件图割获取图像的优化分割,其中,为了引导图割优化的方向,在平滑项中引入彩色梯度信息;最后去除分割结果中的弱边界,获得最终的分割结果.实验结果表明,相对于比较算法,新算法的分割性能有明显提升.
  • [1] Deng Y N, Manjunath B S. Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(8): 800-810
    [2] Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619
    [3] Yang A Y, Wright J, Ma Y, Shankar S S. Unsupervised segmentation of natural images via lossy data compression. Computer Vision and Image Understanding, 2008, 110(2): 212-225
    [4] Kolmogorov V, Zabin R. What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2): 147-159
    [5] Han Shou-Dong, Zhao Yong, Tao Wen-Bing, Sang Nong. Gaussian super-pixel based fast image segmentation using graph cuts. Acta Automatica Sinica, 2011, 37(1): 11-20(韩守东, 赵勇, 陶文兵, 桑农. 基于高斯超像素的快速Graph Cuts图像分割方法. 自动化学报, 2011, 37(1): 11-20)
    [6] Liu Song-Tao, Yin Fu-Liang. The basic principle and its new advances of image segmentation methods based on graph cuts. Acta Automatica Sinica, 2012, 38(6): 911-922(刘松涛, 殷福亮. 基于图割的图像分割方法及其新进展. 自动化学报, 2012, 38(6): 911-922)
    [7] Liu Song-Tao, Wang Hui-Li, Yin Fu-Liang. Interactive ship infrared image segmentation method based on graph cut and fuzzy connectedness. Acta Automatica Sinica, 2012, 38(11): 1735-1750(刘松涛, 王慧丽, 殷福亮. 基于图割和模糊连接度的交互式舰船红外图像分割方法. 自动化学报, 2012, 38(11): 1735-1750)
    [8] Zhou H L, Zheng J M, Wei L. Texture aware image segmentation using graph cuts and active contours. Pattern Recognition, 2013, 46(6): 1719-1733
    [9] Jung C, Kim C. A unified spectral-domain approach for saliency detection and its application to automatic object segmentation. IEEE Transactions on Image Processing, 2012, 21(3): 1272-1283
    [10] Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239
    [11] Kolmogorov V, Rother C. Minimizing non-submodular functions with graph cuts-a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(7): 1274-1279
    [12] Delong A, Osokin A, Isack H, Boykov Y. Fast approximate energy minimization with label costs. International Journal of Computer Vision, 2012, 96(1): 1-27
    [13] Kohli P, Ladicky L, Torr P. Robust higher order potentials for enforcing label consistency. International Journal of Computer Vision, 2009, 82(3): 302-324
    [14] Alahari K, Kohli P, Torr P H S. Dynamic hybrid algorithms for MAP inference in discrete MRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(10): 1846-1857
    [15] Delong A, Veksler O, Boykov Y. Fast fusion moves for multi-model estimation. In: Proceedings of the 12th European Conference on Computer Vision (ECCV). Berlin, Heidelberg: Springer-Verlag, 2012. 370-384
    [16] Kim J S, Hong K S. Color-texture segmentation using unsupervised graph cuts. Pattern Recognition, 2009, 42(5): 735-750
    [17] Li L, Jin L H, Xu X Y, Song E M. Unsupervised color-texture segmentation based on multiscale quaternion gabor filters and splitting strategy. Signal Processing, 2013, 93(9): 2559-2572
    [18] Feng W, Jia J Y, Liu Z Q. Self-validated labeling of Markov random fields for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(10): 1871-1887
    [19] Chen S F, Cao L L, Wang Y M, Liu J Z. Image segmentation by MAP-ML estimations. IEEE Transactions on Image Processing, 2010, 19(9): 2254-2264
    [20] Yang Y, Han S D, Wang T J, Tao W B, Tai X C. Multilayer graph cuts based unsupervised color-texture image segmentation using multivariate mixed student's t-distribution and regional credibility merging. Pattern Recognition, 2013, 46(4): 1101-1124
    [21] Li L, Jin L, Song E, et al., Unsupervised color image segmentation using graph cuts with multi-componments. In: Proceeding of Multispectral Image Processing and Pattern Recognition (MIPPR), Wuhan, China: SPIE, 2013. 89180B-1-89180B-8
    [22] Hoyer P O. Non-negative matrix factorization with sparseness constraints. The Journal of Machine Learning Research, 2004, 5: 1457-1469
    [23] Jin L H, Liu H, Xu X Y, Song E M. Improved direction estimation for Di Zenzo's multichannel image gradient operator. Pattern Recognition, 2012, 45(12): 4300-4311
    [24] Elkan C. Using the triangle inequality to accelerate k-means. In: Proceedings of the 20th International Conference on Machine Learning (ICML). Washington DC, USA: AAAI Press, 2003. 147-153
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出版历程
  • 收稿日期:  2013-05-23
  • 修回日期:  2013-10-09
  • 刊出日期:  2014-06-20

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