Robust K-means algorithm with automatically splitting and merging clusters and its applications for surveillance data

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• 作者：Jingsheng Lei ; Teng Jiang ; Kui Wu ; Haizhou Du…
• 关键词：K ; means ; Robust ; Cluster centers selection ; Automatically splitting and merging ; Data mining ; Video surveillance
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：75
• 期：19
• 页码：12043-12059
• 全文大小：1,353 KB
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721
• 卷排序：75

文摘

With the pervasive of the definition of the smart city, the data volume of the surveillance system, huge number of video surveillance devices is now rapidly expanding. The research to surveillance data mining and analytics has attracted increasing attention due to its applications. Cluster analysis as an important task of data mining in video surveillance has recently been highly explored. K-means algorithm is the most popular and widely-used partitional clustering algorithm in practice. However, traditional k-means algorithm suffers from sensitive initial selection of cluster centers, and it is not easy to specify the number of clusters in advance. In this paper, we propose a robust k-means algorithm that can automatically split and merge clusters which incorporates the new ideas in dealing with huge scale of video data. This novel algorithm not only addresses the sensitivity in selecting initial cluster centers, but also is resilient to the initial number of clusters. The performance is experimentally verified using synthetic and publicly available datasets. The experiments demonstrate the effectiveness and robustness of the proposed algorithm. Moreover, experiment is conducted on a real video surveillance dataset and the result shows that the novel approach can be applicated friendly in video surveillance.