基于半监督信息的截集式可能性C-均值聚类算法

范九伦; 高梦飞; 于海燕; 陈斌斌

doi:10.11999/JEIT200757

基于半监督信息的截集式可能性C-均值聚类算法

doi: 10.11999/JEIT200757

1.
西安邮电大学通信与信息工程学院西安 710121
2.
西安邮电大学电子信息勘验应用技术公安部重点实验室西安 710121

基金项目: 国家自然科学基金(62071378, 62071379, 62071380)，西安邮电大学西邮新星团队(xyt2016-01)

详细信息

作者简介:
范九伦：男，1964年生，教授，研究方向为模式识别与图像处理、模糊信息处理、图像安全技术

高梦飞：男，1994年生，硕士生，研究方向为图像处理

于海燕：女，1982年生，副教授，研究方向为模式识别与图像处理

陈斌斌：男，1994年生，硕士生，研究方向为图像处理

通讯作者:
高梦飞　1143932136@qq.com

中图分类号: TN911.73; TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2020-08-26
- 修回日期: 2021-01-22
- 网络出版日期: 2021-02-23
- 刊出日期: 2021-08-10

Cutset-type Possibilistic C-means Clustering Algorithms Based on Semi-supervised Information

1.
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Key Laboratory of Electronic Information Application Technology for Scene Investigation of Ministry of Public Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (62071378, 62071379, 62071380), New Star Team of Xi’an University of Posts & Telecommunications (xyt2016-01)

摘要

摘要: 截集式可能性C-均值(C-PCM)聚类算法将截集概念引入可能性C-均值(PCM)聚类算法中，明显改善了PCM的聚类中心重合问题，并能够对噪声和奇异点的数据进行有效聚类，但该聚类算法对小目标数据聚类时仍然存在聚类中心偏移的问题。针对此问题，该文将半监督学习机制引入C-PCM的目标函数中，通过部分先验信息来指导聚类过程，提出半监督截集式可能性C-均值(SS-C-PCM)聚类算法。为了提高彩色图像的分割效率和分割准确率，将差分进化超像素(DES)算法获得的图像空间邻域信息融入SS-C-PCM目标函数中，并利用彩色直方图重构目标函数，以降低算法的计算复杂度，进而提出基于差分进化超像素的半监督截集式可能性C-均值(desSS-C-PCM)聚类算法。通过人造数据和彩色图像分割的仿真并与多种相关算法进行对比，表明该文算法能够有效改善小目标数据的聚类效果，提高算法的执行效率。
- 截集式可能性C-均值聚类 /
- 半监督 /
- 超像素 /
- 彩色直方图
Abstract: Cutset-type Possibilistic C-Means clustering (C-PCM) algorithm can significantly reduce the coincident clustering phenomenon of the Possibilistic C-Means clustering (PCM) algorithm by introducing the cut-set concept into the PCM. The C-PCM also has strong robustness to noise and outliers. However, the C-PCM still suffers from the center migration problem for datasets with small targets. In order to solve this problem, a Semi-Supervised Cutset-type Possibility C-Means (SS-C-PCM) clustering algorithm is proposed by introducing the semi-supervised learning mechanism into the objective function of the C-PCM and utilizing some prior information to guide the clustering process. Meanwhile, in order to improve the segmentation efficiency and accuracy of color images, a differential evolutionary superpixel-based Semi-Supervised Cutset-type Possibilistic C-Means (desSS-C-PCM) clustering algorithm is proposed. In the desSS-C-PCM, the Differential Evolutionary Superpixel(DES) algorithm is used to obtain the spatial neighborhood information of an image, which is integrated into the objective function of the semi-supervised C-PCM to improve the segmentation quality. Simultaneously, the color histogram is used to reconstruct the new objective function to reduce the computational complexity of the algorithm. Several experiments of artificial data clustering and color image segmentation show that the proposed algorithm can effectively improve the clustering effect of datasets with small targets and the execution efficiency compared with several related algorithms.
- Cutset-type Possibilistic C-Means clustering (C-PCM) /
- Semi-supervised /
- Superpixel /
- Color histogram

HTML全文

图 1 desSS-C-PCM算法流程图

下载: 全尺寸图片幻灯片

图 2 针对数据集${X_{1600}}$的聚类结果

下载: 全尺寸图片幻灯片

图 3 #124084分割结果图

下载: 全尺寸图片幻灯片

表 1 针对数据集${X_{1600}}$各个算法的中心偏移量以及迭代次数

算法	FCM	SS-FCM	C-PCM	SS-C-PCM
中心偏移量	31.9391	29.8891	23.4551	0.1165
迭代次数	85	60	70	38

下载: 导出CSV

表 2 各个算法的分割准确率对比

图像	FCM	C-PCM	SS-FCM	SS-C-PCM	desSS-C-PCM
#3063	0.7292	0.6540	0.9924	0.9931	0.9939
#3096	0.9859	0.6324	0.9860	0.9921	0.9932
#135069	0.7358	0.5624	0.9919	0.9895	0.9905
#118035	0.9342	0.6970	0.9342	0.9457	0.9754
#124084	0.7486	0.6127	0.7487	0.8049	0.9658
#86016	0.8393	0.6126	0.8396	0.9676	0.9931
#161062	0.8846	0.9285	0.8847	0.9452	0.9885
#113044	0.8352	0.6243	0.8354	0.9521	0.9782
#12003	0.7738	0.5811	0.7740	0.9483	0.9740
#238011	0.8123	0.8090	0.9560	0.9282	0.9637
#101027	0.8840	0.6330	0.8846	0.9212	0.9418
#28075	0.4473	0.3987	0.4456	0.5922	0.9842
#24063	0.8919	0.5965	0.8919	0.8976	0.8920
#253036	0.6192	0.5360	0.6195	0.8966	0.9849
#42044	0.7525	0.8181	0.7526	0.8067	0.8884
#299091	0.6960	0.7950	0.6964	0.9941	0.9945
#113016	0.8168	0.5897	0.8185	0.7376	0.9806
#147091	0.9316	0.6690	0.9317	0.9253	0.9413
#67079	0.8274	0.8312	0.8276	0.8936	0.9918

下载: 导出CSV

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