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Image segmentation application based on incremental learning vector SVM algorithm


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Image segmentation application based on incremental learning vector SVM algorithm
Chen Yuantao12Xu Weihong12Wu Jiaying12Hu Rong1
1.School of Computer Science and Engineering,NUST,Nanjing 210094,China; 2.School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410014,China
support vector machine incremental learning vector support vector machine image segmentation thin narrow set
In order to solve less efficient,longer time-consuming problem of the traditional SVM methods,this paper proposes a support vector machine learning algorithm based on the incremental vector.The algorithm obtains the initial support vector machine classifier by training the sample collection incremental vector learning.This paper streamlines the relevant conditions for initial training sample set to be streamlined narrow set by using the initialization classification,applies the thin narrow set of initial support vector machine classifier in reverse processing,and gets the support vector machine classification devices.The algorithm can significantly reduce the training time of support vector machine and large-capacity data set and has good generalization performance.In order to verify the application of the algorithm,this paper selects relative color images from Berkeley image segmentation data set BSDS500 and Internet experiments to do simulation experiment.The experimental results show that this segmentation process has much less time-consuming than the traditional support vector machine and better segmentation than the manually marked results in Berkeley image segmentation dataset.


[1] Platt J C.Fast training of SVMs using sequential minimal optimization[A].Advances in Kernel MethodsSupport Vector Learning[C].Scholkopf B,Burges C J C,Smola A J,1998:185-208.
[2]Keerthi S,Shevade S,Bhattacharyya C,et al.Improvements to platt's SMO algorithm for SVM classifier design[J].Neural Networks,1999,6(12):783-789.
[3]Zhang Ling,Zhang Bo.A geometrical representation of McCulloch-Pitts neural model and its applications[J].IEEE Transactions on Neural Networks,1999,10(4):925-929.
[4]Todorovic S,Nechyba M.Dynamic trees for unsupervised segmentation and matching of image regions[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(11):1762-1777.
[5]Jiang Zhuolin,Lin Zhe,Davis L S.Label consistent K-SVD:Learning a discriminative dictionary for recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(11):2651-2664.
[6]Badrinarayanan V,Budvytis I,Cipolla R.Semi-supervised video segmentation using tree structured graphical models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(11):2751-2764.
Li Honglian,Wang Chunhua,Yuan Baozong,et al.A learning strategy of SVM used to large training set[J].Chinese Journal of Computers,2004,27(5):715-719.
[8]Canu S,Grandvalet Y,Guigue V,et al.SVM and kernel methods matlab toolbox[EB/OL].http://asi.insa-rouen.fr/~arakotom/toolbox/.2005-12-20.
[9]Arbelaez P,Maire M,Fowlkes C,et al.Contour detection and hierarchical image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(8):898-916.
Zhang Peilin,Qian Linfang,Cao Jianjun,et al.Parameter optimization of support vector machine based on ant colony optimization algorithm[J].Journal of Nanjing University of Science and Technology,2009,33(4):464-468.
Li Min,Luo Hongyan,Zheng Xiaolin,et al.Image segmentation based on improved Otsu algorithm[J].Journal of Nanjing University of Science and Technology,2012,36(2):332-337.


Last Update: 2014-02-28