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

•The level histogram is used with the K-means algorithm for clustering data.•The fast K-means algorithm was effectively applied to image database sets.•The fast K-means algorithm improved the efficiency of the traditional K-means algorithm.•The fast K-means algorithm processed images efficiently as...

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Bibliographic Details
Published in:Expert systems with applications 2014-06, Vol.41 (7), p.3276-3283
Main Authors: Lin, Chuen-Horng, Chen, Chun-Chieh, Lee, Hsin-Lun, Liao, Jan-Ray
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Clusters
Color
Color feature
Computer science
control theory
systems
Data processing. List processing. Character string processing
Exact sciences and technology
Histogram
Histograms
Image retrieval
Information systems. Data bases
K-means
Memory organisation. Data processing
Pattern recognition. Digital image processing. Computational geometry
Retrieval
Software
Training
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Summary:•The level histogram is used with the K-means algorithm for clustering data.•The fast K-means algorithm was effectively applied to image database sets.•The fast K-means algorithm improved the efficiency of the traditional K-means algorithm.•The fast K-means algorithm processed images efficiently as images increases.•The selection of the initial cluster centers affected the performance. 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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.11.017