Image clustering segmentation based on SLIC superpixel and transfer learning

被引：6

作者：

Li X.X. ^{[1
,2
]}

Shen X.J. ^{[1
,2
]}

Chen H.P. ^{[1
,2
]}

Feng Y.C. ^{[1
,2
]}

机构：

[1] College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin

[2] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin

来源：

Pattern Recognition and Image Analysis | 2017年 / 27卷 / 04期

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

fuzzy clustering; image segmentation; SLIC superpixel; transfer learning;

D O I：

10.1134/S1054661817040101

中图分类号：

学科分类号：

摘要：

Traditional fuzzy C-means clustering algorithm has poor noise immunity and clustering results in image segmentation. To overcome this problem, a novel image clustering algorithm based on SLIC superpixel and transfer learning is proposed in this paper. In the proposed algorithm, SLIC superpixel method is used to improve the edge matching degree of image segmentation and enhances the robustness to noise. Transfer learning is adopted to correct the image segmentation result and further improve the accuracy of image segmentation. In addition, the proposed algorithm improves the original SLIC superpixel algorithm and makes the edge of the superpixel more accurate. Experimental results show that the proposed algorithm can obtain better segmentation results. © 2017, Pleiades Publishing, Ltd.

引用

页码：838 / 845

页数：7

共 18 条

[1]

Jiang G.-P., Qin W.-J., Zhou S.-J., Wang C.-M., State-of-the-art in medical image segmentation, Chinese J. Comput., 38, 6, pp. 1222-1242, (2015)

[2]

Xiao M.-S., Wen Z.-C., Zhang J.-W., Wan X.-F., An FCM^clustering algorithm with improved membership function, Control Decision, 30, 12, pp. 2270-2274, (2015)

[3]

Grira N., Crucianu M., Boujemaa N., Active semi-supervised fuzzy clustering, Pattern Recogn., 41, 5, pp. 1834-1844, (2008)

[4]

Zhong C.F., Liu Z., Yang Y., Et al., Improved FCM^algorithm based on neighboring membership constraint for image segmentation, J. Jilin Univ., 31, 6, pp. 627-633, (2013)

[5]

Wang C.Y., Chen J.Z., Wei L.I., Review on superpixel segmentation algorithms, Appl. Res. Comput., 31, 1, pp. 6-12, (2014)

[6]

Wang Z., Lou H., Fan J.L., Fuzzy C-means clustering algorithm with superpixel for image segmentation, Microelectron. Comput., (2014)

[7]

Zhuang F.Z., Luo P., He Q., Et al., Survey on transfer learning research, J. Software, 26, pp. 26-39, (2015)

[8]

Mei C.-H., Zhang Y.-H., Hu X.-G., Et al., A weighted algorithm of inductive transfer learning based on maximum entropy model, J. Comput. Res. Develop., 48, 9, pp. 1722-1728, (2011)

[9]

Zhang Q., Li M., Wang X.S., Et al., Instance-based transfer learning for multi-source domains, Acta Automat. Sinica, 40, 6, pp. 1176-1183, (2014)

[10]

Jiang Y., Deng Z., Wang J., Et al., Transfer generalized fuzzy c-means clustering algorithm with improved fuzzy partitions by leveraging knowledge, Pattern Recogn. Artificial Intellig., 26, 10, pp. 975-984, (2013)

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