To deal with poor robustness in strong noise environment, weak adaptability to complex mixed noise that appear in the existing unsupervised image segmentation models, an improved noise-robust image segmentation model based on One-class SVM (Support Vector Machine) method was proposed. Firstly, a data outlier detection mechanism was constructed based on One-class SVM. Secondly, an outlier degree was introduced into the energy function, so that more accurate image information could be obtained by the proposed model under multiple noise intensities and the failure of weight-descend mechanism in strong noise environment was avoided. Finally, the segmentation contour was driven to the target edge by minimizing the energy function. In noise image segmentation experiments, the proposed model could obtain ideal segmentation results with different types and intensities of noise. Under F1-score metric, the proposed model is 0.2 to 0.3 higher than LCK (Local Correntropy-based K-means) model, and has better stability in strong noise environments. The segmentation convergence time of the proposed model is only slightly longer than that of LCK model by about 0.1 s. Experimental results show that the proposed model is more robust to probabilistic, extreme values and mixed noise without significantly increase of segmentation time, and can segment natural images with noise.
[1] KASS M, WITKIN A, TERZOPOULOS D. Snakes:active contour models[J]. International Journal of Computer Vision, 1988, 1(4):321-331. [2] CHAN T F, VESE L A. Active contour without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2):266-277. [3] LI C, KAO C-Y, GORE J C, et al. Implicit active contours driven by local binary fitting energy[C]//CVPR 2007:Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2007:1-7 [4] ZHANG K, SONG H, ZHANG L. Active contours driven by local image fitting energy[J]. Pattern Recognition, 2010, 43(4):1199-1206. [5] WANG L, PAN C. Robust level set image segmentation via a local correntropy-based K-means clustering[J]. Pattern Recognition, 2014, 47(5):1917-1925. [6] JIANG X-L, WANG Q, HE B, et al. Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints[J]. Neurocomputing, 2016, 207(C):22-35. [7] LI C, XU C, GUI C, et al. Level set evolution without re-initialization:a new variational formulation[C]//CVPR'05:Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2005:430-436. [8] 赵怡,邓红霞,张玲,等.基于最大类间方差的权重自适应活动轮廓模型[J].计算机工程与设计,2018,39(2):486-491.(ZHAO Y, DENG H X, ZHANG L, et al. Weight-self adjustment active contour model based on method of maximum classes square error[J]. Computer Engineering and Design, 2018,39(2):486-491.) [9] 李钢,李海芳,尚方信,等.基于梯度信息的自适应邻域噪声图像分割模型[J].计算机工程,2018,44(5):227-233.(LI G, LI H F, SHANG F X, et al. Noise image segmentation model with adaptive neighborhood based on gradient information[J]. Computer Engineering, 2018, 44(5):227-233.) [10] SCHÖLKOPF B, PLATT J C, SHAWE-TAYLOR J, et al. Estimating the support of a high-dimensional distribution[J]. Neural Computation, 2001, 13(7):1443-1471. [11] 李昊奇,应娜,郭春生,等.基于深度信念网络和线性单分类SVM的高维异常检测[J].电信科学,2018,34(1):34-42.(LI H Q, YING N, GUO C S, et al. High-dimensional outlier detection based on deep belief network and linear one-class SVM[J]. Telecommunications Science, 2018, 34(1):34-42.) [12] 杨成佳.图像去噪及其效果评估若干问题研究[D].长春:吉林大学,2016:4-5.(YANG C J. Research on image denoising and its effect evaluation[D]. Changchun:Jilin University, 2016:4-5.) [13] LeCUN Y, BOTTOU L, BENGIO Y, et al. MNIST[DB/OL]. (2012-01-01)[2018-08-27]. http://yann.lecun.com/exdb/mnist/. [14] BORENSTEIN E. Weizmann horse database[DB/OL]. (2005-01-19)[2018-08-27]. http://www.msri.org/m/people/members/eranb/.