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Gesture segmentation based on a two-phase estimation of distribution algorithm

Liu, Ke and Gong, Dunwei and Meng, Fanlin and Chen, Huanhuan and Wang, Gai-Ge (2017) 'Gesture segmentation based on a two-phase estimation of distribution algorithm.' Information Sciences, 394-39. pp. 88-105. ISSN 0020-0255

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Abstract

A multi-objective optimization model for the problem of gesture segmentation is formulated, and a method of solving the model based on a two-phase estimation of distribution algorithm is presented. When building the model, the positions of a series of pixels are taken as the decision variable, and the differences between the colors of pixels and those of a hand are taken as objective functions. A method of gesture segmentation based on a two-phase estimation of distribution algorithm is proposed according to the correlation among the positions of pixels. The method divides the solution of the problem based on evolutionary optimization into two phases, and uses different estimation of distribution algorithms in different phases. In the first phase, the probability model of candidates is formulated by a number of intervals given the fact that the positions of hand pixels distribute in several intervals. In the second phase, the probability model of candidates is built through a series of segments since the positions of hand pixels further distribute around curves. A series of pixels constituting a hand region are obtained based on sampling by the above probability models. The proposed method is applied to 2515 problems of gesture segmentation, and is compared with the existing methods. The experimental results demonstrate the effectiveness of the proposed method.

Item Type: Article
Uncontrolled Keywords: Gesture segmentation; Estimation of distribution algorithm; Two-phase; Probability model; Sampling
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Jul 2021 15:18
Last Modified: 23 Sep 2022 19:31
URI: http://repository.essex.ac.uk/id/eprint/30715

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