Real-time K-Means Clustering for Color Images on Reconfigurable Hardware

Authors:
Tsutomu Maruyama
Affiliations:
University of Tsukuba, Japan
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

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Abstract

K-means clustering is a very popular clustering technique, which is used in numerous applications. However, clustering is a time consuming task, particularly for large dataset, and large number of clusters. In this paper, we show that real-time k-means clustering can be realized for large size color images (24-bit full color RGB) and large number of clusters (up to 256) using an off-the-shelf FPGA (Field Programmable Gate Arrays) board. In our current implementation with one FPGA, the performance for 512 脳 512 and 640 脳 480 pixel images is more than 30 fps, and 20 - 30 fps for 756 脳 512 pixel images in average when dividing to 256 clusters.