An interactive image segmentation method in hand gesture recognition

Disi Chen, Gongfa Li, Ying Sun, Jianyi Kong, Guozhang Jiang, Heng Tang, Zhaojie Ju, Hui Yu, Honghai Liu

Research output: Contribution to journalArticlepeer-review

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In order to improve the recognition rate of hand gestures a new interactive image segmentation method is presented in hand gesture recognition, and popular methods, e.g. Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally 5 kinds of hand gestures in different background are tested on our experiment platform, and the sparse representation algorithm is used and proves that the segmentation of hand gesture image helps to improve the recognition accuracy.
Original languageEnglish
Pages (from-to)1-16
Number of pages16
Issue number2
Publication statusPublished - 27 Jan 2017


  • image segmentation
  • Gibbs Energy
  • min-cut/max-flow algorithm
  • sparse representation


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