Generalized Transport Mean Shift algorithm for ubiquitous intelligence

Abstract

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.

Keywords

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

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