Efficient fuzzy c-means clustering for image data

Yung-Sheng Chen; Bor-Tow Chen

doi:10.1117/1.1879012

1 January 2005 Efficient fuzzy c-means clustering for image data

Yung-Sheng Chen, Bor-Tow Chen

Author Affiliations +

Journal of Electronic Imaging, Vol. 14, Issue 1, 013017 (January 2005). https://doi.org/10.1117/1.1879012

Abstract

The clustering process can be quite slow when there is a large data set to be clustered. We investigate four efficient fuzzy c-means clustering methods qFCMs, based on the quad-tree application to multispectral image feature compression and/or an aggregation process to reduce the number of exemplars for image analysis. An image is first partitioned into multiresolution blocks with variable size to extract the representative ones by homogeneity criteria. The blocks can be represented by a mean or fuzzy number to represent the image information. The first algorithm qFCM_b is performed by applying only the representative blocks to a weighted FCM, which can speed up the clustering. To further improve the clustering efficiency, the reduction is done by aggregating similar examples and using a weighted exemplar in the clustering process (qFCM_ba). Based on the same processes used in qFCM_b and qFCM_ba, nonhomogeneous regions including pixel information can also be supplemented to refine the clustering results, which are termed qFCM_p and qFCM_pa, respectively. Because of the merit of higher efficiency with the aggregation process, we recommend qFCM_ba and qFCMpa. A set of 14 images is used for experiments, comparison, and discussion. Performances are reported by the mean reduction rate, speedup, mean correspondence rate, and root mean square error. Results show that the mean reduction rate of both qFCM_ba and qFCM_pa can be as high as 98% reduction in sample size. Average speedups of as much as 40 to 150 times (100 to 200 times) a traditional implementation FCM are obtained using qFCM_pa (qFCM_ba), while producing partitions that are equivalent to those produced by FCM. On the measure of root mean square error, qFCM_ba is the better choice, as indicated in the experiment of clustering a noisy image.

©(2005) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Yung-Sheng Chen and Bor-Tow Chen "Efficient fuzzy c-means clustering for image data," Journal of Electronic Imaging 14(1), 013017 (1 January 2005). https://doi.org/10.1117/1.1879012

Published: 1 January 2005

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