NDLI: An FPGA implementation of real-time K-means clustering for color images

Content Provider	Springer Nature Link
Author	Saegusa, Takashi Maruyama, Tsutomu
Copyright Year	2007
Abstract	K-means clustering is a very popular clustering technique, which is used in numerous applications. In the k-means clustering algorithm, each point in the dataset is assigned to the nearest cluster by calculating the distances from each point to the cluster centers. The computation of these distances is a very time-consuming task, particularly for large dataset and large number of clusters. In order to achieve high performance, we need to reduce the number of the distance calculations for each point efficiently. In this paper, we describe an FPGA implementation of k-means clustering for color images based on the filtering algorithm. In our implementation, when calculating the distances for each point, clusters which are apparently not closer to the point than other clusters are filtered out using kd-trees which are dynamically generated on the FPGA in each iteration of k-means clustering. The performance of our system for 512 × 512 and 640 × 480 pixel images (24-bit full color RGB) is more than 30 fps, and 20–30 fps for 756 × 512 pixel images in average when dividing to 256 clusters.
Starting Page	309
Ending Page	318
Page Count	10
File Format	PDF
ISSN	18618200
Journal	Journal of Real-Time Image Processing
Volume Number	2
Issue Number	4
e-ISSN	18618219
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-11-22
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	K-means clustering Color images Real-time FPGA Signal, Image and Speech Processing Pattern Recognition Computer Graphics Multimedia Information Systems Image Processing and Computer Vision
Content Type	Text
Resource Type	Article
Subject	Information Systems

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