Real-time K-Means Clustering for Color Images on Reconfigurable Hardware
- 1 January 2006
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 18th International Conference on Pattern Recognition (ICPR'06)
- Vol. 2, 816-819
- https://doi.org/10.1109/icpr.2006.961
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 times 512 and 640 times 480 pixel images is more than 30fps, and 20-30 fps for 756 times 512 pixel images in average when dividing to 256 clustersKeywords
This publication has 4 references indexed in Scilit:
- Fast and Exact Out-of-Core K-Means ClusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Hardware-driven adaptive k-means clustering for real-time video imagingIEEE Transactions on Circuits and Systems for Video Technology, 2005
- An efficient k-means clustering algorithm: analysis and implementationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
- Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardwarePublished by Association for Computing Machinery (ACM) ,2001