.png){kind=icon}
.png){kind=icon}
Return
Fast K-means algorithm based on a level histogram for image retrieval
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2013.11.017.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
K-means
Histogram
Image retrieval
Color feature
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W