Fast K-means algorithm based on a level histogram for image retrieval

  • Authors:

  • Chuen-Horng Lin;Chun-Chieh Chen;Hsin-Lun Lee;Jan-Ray Liao

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, No. 129, Sec. 3, Sanmin Rd., Taichung, Taiwan, ROC;Department of Electrical Engineering, National Chung Hsing University, No. 250, Kuokuang Rd., Taichung, Taiwan, ROC;Department of Electrical Engineering, National Chung Hsing University, No. 250, Kuokuang Rd., Taichung, Taiwan, ROC;Department of Electrical Engineering, National Chung Hsing University, No. 250, Kuokuang Rd., Taichung, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2014

Quantified Score

Hi-index 12.05

Visualization

Abstract

In image retrieval, the image feature is the main factor determining accuracy; the color feature is the most important feature and is most commonly used with a K-means algorithm. To create a fast K-means algorithm for this study, first a level histogram of statistics for the image database is made. The level histogram is used with the K-means algorithm for clustering data. A fast K-means algorithm not only shortens the length of time spent on training the image database cluster centers, but it also overcomes the cluster center re-training problem since large numbers of images are continuously added into the database. For the experiment, we use gray and color image database sets for performance comparisons and analyzes, respectively. The results show that the fast K-means algorithm is more effective, faster, and more convenient than the traditional K-means algorithm. Moreover, it overcomes the problem of spending excessive amounts of time on re-training caused by the continuous addition of images to the image database. Selection of initial cluster centers also affects the performance of cluster center training.