Fast fuzzy clustering of infrared images

S. Eschrich, Jingwei Ke, L.O. Hall, D.B. Goldgof
Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)  
Clustering is an important technique for unsupervised image segmentation. The use of fuzzy c-means clustering can provide more information and better partitions than traditional c-means. In image processing, the ability to reduce the precision of the input data and aggregate similar examples can lead to significant data reduction and correspondingly less execution time. This paper discusses brFCM, a data reduction fuzzy c-means clustering algorithm. The algorithm is described and several key
more » ... and several key implementation issues are discussed. Performance speedup and correspondence to a typical FCM implementation are presented from a dataset of 172 infrared images. Average speedups of 59 times traditional FCM were obtained using brFCM, while producing identical cluster output relative to FCM.
doi:10.1109/nafips.2001.944766 fatcat:42tbabhbffdcbjzlt4jxfqcul4