Fast K-means algorithm based on a level histogram for image retrieval |
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| Affiliation: | 1. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, No. 129, Sec. 3, Sanmin Rd., Taichung, Taiwan, ROC;2. Department of Electrical Engineering, National Chung Hsing University, No. 250, Kuokuang Rd., Taichung, Taiwan, ROC;1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City 701, Taiwan;2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung City 811, Taiwan;3. Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung City 804, Taiwan;4. Department of Information Management, Southern Taiwan University of Science and Technology, Tainan City 710, Taiwan;1. Department of Software Engineering, Faculty of Telecommunication and Information Engineering, University of Engineering and Technology, Taxila, Pakistan;2. School of Electronics and Computer Science, University of Southampton, Highfield Campus, Southampton SO17 1BJ, United Kingdom;1. Department of Business Administration, Lunghwa University of Science and Technology, Taiwan;2. Department of Finance, MingDao University, Taiwan;3. Business School, the University of Nottingham, United Kingdom;1. Department of Information Management at Fortune Institute of Technology, Kaohsiung, Taiwan;2. Thecus Technology Corporation, Taiwan;3. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan |
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| 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. |
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| Keywords: | K-means Histogram Image retrieval Color feature |
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