Real-Time Segmentation of Color Images based on the K-means Clustering on FPGA

Published 1 December 2007

Takashi Saegusa, Tsutomu Maruyama

DOI10.1109/fpt.2007.4439278

Citations9

TL;DR

In this paper, the implementation of a segmentation method of color images based on the k-means clustering, where the regions can be recognized in parallel with each iteration of the k/s clustering algorithm.

Abstract

In this paper, we describe a segmentation method of color images based on the k-means clustering. With a k-means clustering algorithm, we can reduce the number of colors in a given image to K while maintaining the quality of the image. Based on these K colors, we can segment color images by recognizing contiguous pixels of the same color as a region. However, the k-means clustering is a very time consuming task, particularly for large size images and large number of clusters. Therefore, in order to use a k-means clustering algorithm for image segmentation, we need to recognize the regions in parallel with the k-means clustering algorithm. In our implementation, the regions can be recognized in parallel with each iteration of the k-means clustering algorithm.

Keywords

Computer Science

IEEE Transactions on Pattern Analysis and Machine IntelligenceAn efficient k-means clustering algorithm: analysis and implementation

5,568 Citations2002Tapas Kanungo, David M. Mount +4 more

This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.

Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware

126 Citations2001Mike Estlick, Miriam Leeser +2 more

In mapping the k-means algorithm to FPGA hardware, this work examined algorithm level transforms that dramatically increased the achievable parallelism and also examined the effects of using fixed precision and truncated bit widths in the algorithm.

Hyperspectral images clustering on reconfigurable hardware using the k-means algorithm

47 Citations2004A.Gda.S. Filho, Alejandro C. Frery +7 more

An automatic and parameterized implementation for hyperspectral images has been developed in a hardware/software codesign approach and an unsupervised clustering technique k-means that uses the Euclidian distance to calculate the pixel to centers distance was used as a case study to validate the methodology.

Fast and Exact Out-of-Core K-Means Clustering

41 Citations2005Anurag Goswami, Ruoming Jin +1 more

This paper presents a new algorithm which typically requires only one or a small number of passes on the entire dataset, and provably produces the same cluster centers as reported by the original k-means algorithm.

An FPGA Implementation of K-Means Clustering for Color Images Based on Kd-Tree

37 Citations2006Takashi Saegusa, Tsutomu Maruyama

In this paper, an FPGA implementation of k-means clustering for color images is described and the performance for 512 times 512 and 640 times 480 pixel images (24-bit full color RGB) is more than 30 fps.

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

36 Citations2006Tsutomu Maruyama

It is shown that real-time k-means clustering can be realized for large size color images and large number of clusters using an off-the-shelf FPGA (field programmable gate arrays) board.

IEEE Transactions on Circuits and Systems for Video TechnologyHardware-driven adaptive k-means clustering for real-time video imaging

25 Citations2005B. Maliatski, Orly Yadid-Pecht

IEEE Transactions on Circuits and Systems for Video TechnologyHardware-driven adaptive k-means clustering for real-time video imaging

24 Citations2005B. Maliatski, Orly Yadid-Pecht

A new adaptive k-means clustering algorithm for real-time video imaging is presented, where a weighted contribution of both pixel intensity and distance between the pixels is used for cluster identification.