Space Feature Based Spectral Clustering for Noisy Image Segmentation
LIU Han-Qiang1, ZHAO Feng2
1.School of Computer Science,Shaanxi Normal University,Xian 710062 2.School of Telecommunications and Information Engineering,Xian University of Posts and Telecommunications,Xian 710061
Abstract To overcome the problem that the traditional spectral clustering is easily influenced by image noise while applied to noisy image segmentation, a space feature based spectral clustering algorithm for noise image segmentation is proposed. In this method, gray value, local spatial information and non-local spatial information of each pixel are utilized to construct a 3-dimensional feature dataset. Then, the space compactness function is introduced to compute the similarity between each feature point and its K nearest neighbors. Finally, the final image segmentation result is obtained by spectral clustering algorithm. Some noisy artificial images, nature images and synthetic aperture radar images are utilized and normalized. Cut, FCM_s and Nystrom method are compared with the proposed method in the experiments. The experimental results show that the proposed method is robustness and obtains the satisfying segmentation result.
[1] Gonzalez R C,Woods R E.3rd Edition.Digital Image Processing.Upper Saddle River,USA: Prentice Hall,2008 (Gonzalez R C,Woods R E,著.阮秋琦,阮字智,译.数字图像处理.北京:电子工业出版杜,2003) [2] Chen Songcan,Zhang Daoqiang.Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-Induced Distance Measure.IEEE Trans on Systems,Man and Cybernetic,2004,34(4): 1907-1916 [3] Fiedler M.Algebraic Connectivity of Graphs.Czechoslovak Mathematical Journal,1973,23(98): 298-305 [4] Shi J,Malik J.Normalized Cuts and Image Segmentation.IEEE Trans on Pattern Analysis and Machine Intelligence,2000,22(8): 888-905 [5] Hendrickson B,Leland R.An Improved Spectral Graph Partitioning Algorithm for Mapping Parallel Computations.SIAM Journal on Scientific Computing,1995,16(2): 452-469 [6] Hagen L,Kahng A B.New Spectral Methods for Ratio Cut Partitioning and Clustering.IEEE Trans on Computer-Aided Design,1992,11(9): 1074-1085 [7] Liu Hanqiang,Jiao Licheng,Zhao Feng.Unsupervised Texture Image Segmentation Using Multilayer Data Condensation Spectral Clustering.Journal of Electronic Imaging,2010,19(3): 031203 [8] Dhillon I S.Co-Clustering Documents and Words Using Bipartite Spectral Graph Partitioning // Proc of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco,USA,2001: 269-274 [9] Wang Ling,Bo Liefeng,Jiao Licheng.Density-Sensitive Semi-Supervised Spectral Clustering.Journal of Software,2007,18(10): 2412-2422 (in Chinese) (王 玲,薄列峰,焦李成.密度敏感的半监督谱聚类.软件学报,2007,18(10): 2412-2422) [10] Zhao Feng,Jiao Licheng,Liu Hanqiang.Semi-Supervised Eignvector Selection for Spectral Clustering.Pattern Recognition and Artificial Intelligence,2011,24(1): 48-49 (in Chinese) (赵 凤,焦李成,刘汉强.半监督谱聚类特征向量选择算法.模式识别与人工智能,2011,24(1): 48-49) [11] Zhao Feng,Jiao Licheng,Liu Hanqiang,et al.Spectral Clustering with Eigenvector Selection Based on Entropy Ranking.Neurocomputing,2010,73(10/11/12): 1704-1717 [12] Fowlkes C,Belongie S,Chung F,et al.Spectral Grouping Using the Nystrm Method.IEEE Trans on Pattern Analysis and Machine Intelligence,2004,26(2): 214-225 [13] Macaire L,Vandenbroucke N,Postaire J G.Color Image Segmentation by Analysis of Subset Connectedness and Color Homogeneity Properties.Computer Vision and Image Understanding,2006,102(1): 105-116 [14] Buades A,Coll B,Morel J M.A Non-Local Algorithm for Image Denoising // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition.San Diego,USA,2005,II: 60-65 [15] Ng AY,Jordan M I,Weiss Y.On Spectral Clustering: Analysis and an Algorithm // Dietterich T,Becker S,Ghahramani Z,eds.Advances in Neural Information Processing Systems.Cambridge,USA: MIT Press,2002: 849-856 [16] Arya S,Mount D M,Netanyahu N S,et al.An Optimal Algorithm for Approximate Nearest Neighbor Searching Fixed Dimensions.Journal of the ACM,1998,45(6): 891-923
2012, Vol. 25 
