An Adaptive Image Segmentation Algorithm Based on AP Clustering

Chun Hua Qian; He Qun Qiang; Sheng Rong Gong

doi:10.4028/www.scientific.net/AMR.1078.405

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1078An Adaptive Image Segmentation Algorithm Based on...

An Adaptive Image Segmentation Algorithm Based on AP Clustering

Abstract:

For the shortcomings of classsical clustering algorithms: must assign the number of clusters, the initial cluster center and initial membership degree matrix, ignore the spatial structure information, we propose a novel adaptive image segmentation algorithm based on AP clustering (AAP). We calculate the AP clustering preference parameter of different images adaptively, use the color-texture feature vector to clustering segmentation. Compare to K-Means and FCM, the new algorithm is more accurate and robust.

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Periodical:

Advanced Materials Research (Volume 1078)

Pages:

405-408

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1078.405

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Online since:

December 2014

Authors:

Chun Hua Qian*, He Qun Qiang, Sheng Rong Gong

Keywords:

Affinity Propagation Clustering, FCM, Image Segmentation, K-Means

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* - Corresponding Author

References

[1] C Zhang and P Wang. A New Method of Color Image Segmentation Based on Intensity and Hue Clustering [J]. Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, 2000: 3617-3620.

DOI: 10.1109/icpr.2000.903620

Google Scholar

[2] D L Pham. Spatial models for fuzzy clustering. Computer Vision and Image Understanding, 2001, 84(2): 285-297.

DOI: 10.1006/cviu.2001.0951

Google Scholar

[3] C Rosenberger, K Chehdi. Unsupervised clustering method with optimal estimation of the number of clusters: application to image segmentation[C]. Proceedings of 15th International Conference on Pattern Recognition. Barcelona Spain. 2000, 1: 656-659.

DOI: 10.1109/icpr.2000.905473

Google Scholar

[4] B J Frey, D Dueck. Clustering by passing messages between data points [J]. Science, 2007, 315: 972-976.

DOI: 10.1126/science.1136800

Google Scholar

[5] James.Z. Wang. http: /wang. ist. psu. edu/IMAGE.

Google Scholar

[6] Zhang XiaoFeng, Zhang CaiMing, Tang WenJing, Wei ZhenWen. Medical image segmentation using improved FCM[J]. Science China Information Sciences, 2012, Vol. 55(5), pp.1052-1061.

DOI: 10.1007/s11432-012-4556-0

Google Scholar

[7] Zeng Shan, Tong Xiaojun, Sang Nong, Huang Rui. A study on semi-supervised FCM algorithm[J]. Knowledge and Information Systems, 2013, Vol. 35(3), pp.585-612.

DOI: 10.1007/s10115-012-0521-x

Google Scholar

[8] Kannan S. R, Devi R, Ramathilagam S, Takezawa K. Effective FCM noise clustering algorithms in medical images[J]. Computers in Biology and Medicine, 2013, Vol. 43(2), pp.73-83.

DOI: 10.1016/j.compbiomed.2012.10.002

Google Scholar