Generalized Transport Mean Shift algorithm for ubiquitous intelligence

作     者：Sunat, Khamron Padungweang, Panida Chiewchanwattana, Sirapat 

作者机构：Khon Kaen Univ Dept Comp Sci Fac Sci Khon Kaen 40002 Thailand 

出 版 物：《SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL》 (仿真)

年 卷 期：2012年第88卷第10期

页      面：1202-1215页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Computational Science Research Group (COSRG) Faculty of Science, Khon Kaen University (COSRG-SCKKU) Higher Education Research Promotion and National Research University Project of Thailand, Office of the Higher Education Commission, through the Cluster of Research to Enhance the Quality of Basic Education 

主　　题：Mean Shift algorithm agglomerative mean shift clustering Generalized Transport Mean Shift algorithm image segmentation clustering mode seeking ubiquitous intelligence 

摘      要：Much research has been conducted recently relating to ubiquitous intelligent computing. Ubiquitous intelligence-enabled techniques, such as clustering and image segmentation, have focused on the development of intelligence methodologies. In this paper, a simultaneous mode-seeking and clustering algorithm called the Generalized Transport Mean Shift (GTMS) was introduced. The data points were designated as the transporter-trailer characteristic. The important concept of transportation was used to solve the problem of redundant computations of mode-seeking algorithms. The time complexity of the GTMS algorithm is much lower than that of the Mean Shift (MS) algorithm. This means it is able to be used in a problem that has a very high data point, in particular, the segmentation of images containing the green vegetation. The proposed algorithm was tested on clustering and image-segmentation problems. The experimental results showed that the GTMS algorithm improves upon the existing algorithms in terms of both accuracy and time consumption. The GTMS algorithm s highest speed is also 333.98 times faster than that of the standard MS algorithm. The redundancy computation can be reduced by omitting more than 90% of the data points at the third iteration of the mode-seeking process. This is because GTMS algorithm mainly reduces the data in the mode-seeking process. Thus, use of the GTMS algorithm would allow for the building of an intelligent portable device for surveying green vegetables in a ubiquitous environment.