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基于SLIC超像素分割的图分割算法 被引量:11

A Graph Partitioning Algorithm Based on SLIC Superpixels
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摘要 图像分割是对图像进行分析和理解的关键步骤,是计算机视觉的基本技术之一.计算复杂度是评判一个图像分割算法好坏的重要标准,因此降低算法的计算复杂度是当前图像分割领域的主要任务之一.本文提出了一种基于SLIC超像素分割的图像分割方法.该方法利用SLIC算法生成超像素,通过构造相应的相似性矩阵,有效降低了Ncut分割算法的计算复杂度,大幅度缩短了Ncut算法的运行时间.由于SLIC超像素分割算法的准确性与高效性,在进行三类自然图像分割实验时,本文提出的方法无论在分割效果,还是在运行时间上,都要明显优于Ncut分割方法及它的改良算法. Image segmentation is a key step in the analysis and understanding of the image,and it is also one of the basic techniques in computer vision field. Computational complexity is an important criterion to judge the quality of an image segmentation algorithm, therefore,it is one of the main tasks to reduce the computational complexity of algorithm in the field of image segmentation. An image segmentation method based on SLIC superpixels is proposed in this paper. This new algorithm generates the super-pixels using SLIC algorithm, and reduces effectively the computational complexity of Ncut algorithm via constructing a corresponding similarity matrix. Furthermore, the new algorithm can reduce greatly the running time of Ncut algorithm. Because of the accuracy of SLIC algorithm, the experiments of three natural images demonstrate that our algorithm is better than Ncut algorithm and its improved algorithm no matter on segmentation results or running time.
作者 赵渊 彭济根 高义 ZHAO Yuan;PENG Ji-gen;GAO Yi(School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049;School of Mathematics and Information Science, Beifang University of Nationalities, Yinchuan 750021)
出处 《工程数学学报》 CSCD 北大核心 2016年第5期441-449,共9页 Chinese Journal of Engineering Mathematics
基金 国家自然科学基金(11131006) 国家民委科学研究项目(14BFZ002)~~
关键词 图像分割 Ncut分割算法 相似性矩阵 SLIC算法 image segmentation Ncut algorithm similarity matrix SLIC algorithm
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参考文献16

  • 1Shapiro L, Stockman G C. Computer Vision[M]. New Jersey: Prentice Hall, 2001.
  • 2Zhu S, Xia X, Zhang Q, et al. An image segmentation algorithm in image processing based on threshold segmentation[C].International IEEE Conference on Signal-Image Technologies and Internet-Based System,2007: 673-678.
  • 3Patil R V, Jondhale K C. Edge based technique to estimate number of clusters in k-means color imagesegmentation[C].International IEEE Conference on Computer Science and Information Technology, 2010:117-121.
  • 4Chen G, Hu T, Guo X, et al. A fast region-based image segmentation based on least square method[C].International IEEE Conference on Systems, Man and Cybernetics, 2009: 972-977.
  • 5Zhao W, Zhang J, Li P, et al. Study of image segmentation algorithm based on textural features and neuralnetwork[C].International Conference on Intelligent Computing and Cognitive Informatics, 2010: 300-303.
  • 6Wu Z, Leahy R. An optimal graph theoretic approach to data clustering: theory and its application toimage segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(11):1101-1113.
  • 7Shi J, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2000, 22(8): 888-905.
  • 8Choong M Y, Liau C F, Mountstephens J, et al. Multistage image clustering and segmentation withnormalized cuts[C].International Conference on Intelligent Systems, Modelling and Simulation, 2012:362-367.
  • 9Ren X, Malik J. Learning a classification model for segmentation[C].International IEEE Conference onComputer Vision, 2003: 10-17.
  • 10王春瑶,陈俊周,李炜.超像素分割算法研究综述[J].计算机应用研究,2014,31(1):6-12. 被引量:115

二级参考文献40

  • 1REN Xiao-fimg, MAI,IK J. Learning a classification model for seg- mentation[ C ]//Proc of the 9th IEEE International Conference on Computer Vision. Washington DC :IEEE Computer Society ,2(X)3 : 10-17.
  • 2FEIZENSWALB P F, HUTFENLOCHER D P. Efficient graph-based image segmentation [ J ]. International Journal of Computer Vision, 2004, 59(2):167-181.
  • 3SHI Jian-bo, MALIK J. Normalized cuts and image segmentation [C]//Proc of IEEE Computer Society Conference on Computer Vi-sion and Pattern Recognition. Washingtan DC:IEEE Camputer Socie- ty, 1997:731-737.
  • 4SHI Jian-bo, MAL1K J. Normalized cuts and image segmentation[ J ]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8) :888-905.
  • 5MOORE A, PRINCE S, WARRELI. J, et al. Superpixel lattices [ C]//Proc of IEEE Conference on Computer Vision and Pattern Rec- ognition. 2008 : 1-8.
  • 6LIU Ming-yu, TUZEL O, RAMALINGAM S,et al. Entropy rate su- perpixel segmentation [ C ]//Proc of IEEE Conference on Computer Vision and Pattern Recognition. 2011:2097-2104.
  • 7VINCENT L, SOILLE P. Watersheds in digital spaces: an efficient algoritlml based on inlmeision simulations[ J]. IEEE Trans on Pat- tern Analysis and Machine Intelligence, 1991, 13 (6) : 583-598.
  • 8COMANICIU D, MEER P. Mean shift: a rnhust approrah toward fea- ture space analysis[ J ]. IEEE Trans on Pattern Analysis and Ma- chine Intelligence, 2002, 24(5): 603-619.
  • 9VEDALDI A, SOATTO S. Quick shift and kernel methods for mode seeking [ M ]//Computer Vision. Berlin: Springer-Verlag, 2008: 705-718.
  • 10LEVINSHTEIN A, STERE A, KUTULAKOS K N, et al. Turbotfi- xels: fast superpixels using geometric flows [ J ]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31 (12): 2290- 2297.

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