30 October 2009 Local manifold spectral clustering with FCM data condensation
Author Affiliations +
Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961Y (2009) https://doi.org/10.1117/12.832637
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
In this paper, a novel local manifold spectral clustering with fuzzy c-means (FCM) data condensation is presented. Firstly, a multilayer FCM data condensation method is performed on the original data to contain a condensation subset. Secondly, the spectral clustering algorithm based on the local manifold distance measure is used to realize the classification of the condensation subset. Finally, the nearest neighbor method is adopted to obtain the clustering result of the original data. Compared with the standard spectral clustering algorithm, the novel method is more robust and has the advantages of effectively dealing with the large scale data. In our experiments, we first analyze the performances of multilayer FCM data condensation and local manifold distance measure, then apply our method to solve image segmentation and the large Brodatz texture images classification. The experimental results show that the method is effective and extensible, and especially the runtime of this method is acceptable.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hanqiang Liu, Hanqiang Liu, Licheng Jiao, Licheng Jiao, Feng Zhao, Feng Zhao, } "Local manifold spectral clustering with FCM data condensation", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961Y (30 October 2009); doi: 10.1117/12.832637; https://doi.org/10.1117/12.832637

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