Abstract
Mixture models have been used as efficient techniques in the application of image segmentation. In order to segment images automatically without knowing the number of true image components, the framework of Dirichlet process mixture model (DPMM, also known as the infinite mixture model) has been introduced into conventional mixture models. In this paper, we propose a novel approach for image segmentation by considering the truncated Dirichlet Process of Student’s t-mixture model (tDPSMM). We also develop a novel Expectation Maximization (EM) algorithm for parameter estimation in our model. The proposed model is tested on the application of images segmentation with both brain MR images and natural images. According to the experimental results, our method can segment images effectively and automatically by comparing it with other state-of-the-art image segmentation methods based on mixture models.
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Acknowledgements
This work was supported by the Grant of the National Science Foundation of China (No.61175121, 61502183), the Grant of the National Science Foundation of Fujian Province (No.2013J06014), the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (No.ZQN-YX108), the Scientific Research Funds of Huaqiao University (No.600005-Z15Y0016), the National Natural Science Foundation of China (61502183), the Scientific Research Funds of Huaqiao University (600005-Z15Y0016) and Subsidized Project for Cultivating Postgraduates’ Innovative Ability in Scientific Research of Huaqiao University (Project No. 1400214003,1400214009).
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Li, L., Fan, W., Du, J., Wang, J. (2016). A Novel Image Segmentation Approach Based on Truncated Infinite Student’s t-mixture Model. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_26
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DOI: https://doi.org/10.1007/978-3-319-42297-8_26
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