|Communications on Applied Electronics|
|Foundation of Computer Science (FCS), NY, USA|
|Volume 7 - Number 30|
|Year of Publication: 2019|
|Authors: Anuradha M. G. Basavaraj L.|
Anuradha M. G. Basavaraj L. . FPGA Implementation of Forgy’s K-Means Clustering for Real Time Image Analysis. Communications on Applied Electronics. 7, 30 ( Aug 2019), 5-10. DOI=10.5120/cae2019652832
Partitioning the image into meaningful groups is one of the major task in image analysis which can be achieved using the unsupervised clustering algorithm. K-means algorithm is one of the popular unsupervised clustering algorithm. The K-means algorithm is time-consuming and requires intensive computation for a large data set as the input is compared with all the centroids. Also, the data needs to be stored internally due to iterative re-assignment process. An architecture to enhance the speed of clustering operation using minimal hardware for K-means clustering without any internal storage is proposed and implemented using Virtex 6 FPGA. A new methodology is proposed to reduce the distance computation. The performance of the architecture is 203fps for a grayscale image size of 256X256 and 102fps for a grayscale image of size 512X512. This shows that the proposed architecture can be used for real time image segmentation.