LAI Yuping, PING Yuan, HE Wenda, WANG Baocheng, WANG Jingzhong, ZHANG Xiufeng. Variational Bayesian Inference for Finite Inverted Dirichlet Mixture Model and Its Application to Object Detection[J]. Chinese Journal of Electronics, 2018, 27(3): 603-610. doi: 10.1049/cje.2018.03.003
Citation: LAI Yuping, PING Yuan, HE Wenda, WANG Baocheng, WANG Jingzhong, ZHANG Xiufeng. Variational Bayesian Inference for Finite Inverted Dirichlet Mixture Model and Its Application to Object Detection[J]. Chinese Journal of Electronics, 2018, 27(3): 603-610. doi: 10.1049/cje.2018.03.003

Variational Bayesian Inference for Finite Inverted Dirichlet Mixture Model and Its Application to Object Detection

doi: 10.1049/cje.2018.03.003
Funds:  This work is supported by the National Natural Science Foundation of China (No.51335004, No.61363085, No.61303232), the Project of Action Plan Powerful School with Talents in North China University of Technology (No.XN018022), the Project of Science and Technology Innovation Service Capacity Building Project (No.PXM2017-014212-000002), the Program for Science & Technology Innovation Talents in Universities of Henan Province (No.18HASTIT022), and the Foundation of Henan Educational Committee (No.16A520025, No.18A520047).
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  • Corresponding author: ZHANG Xiufeng (corresponding author) received the Ph.D. degree in mechatronics engineering from Harbin Institute of Technology, Harbin, China, in 2004. He has been a senior scientist at National Research Center for Rehabilitation Technical Aids, Beijing, China, since 2014. His research interests include robotics, pattern recognition, and computer vision. (Email:zhangxiufeng@hit.edu.cn)
  • Received Date: 2016-11-07
  • Rev Recd Date: 2017-04-26
  • Publish Date: 2018-05-10
  • As a variant of Finite mixture model (FMM), finite Inverted Dirichlet mixture model (IDMM) can not avoid the conventional challenges, such as how to select the appropriate number of mixture components based on the observed data. Towards easing these issues, we propose a variational inference framework for learning IDMM which has been proved to be an efficient tool for modeling vectors with positive elements. Compared with the conventional Expectation maximization (EM) algorithm commonly used for learning FMM, the proposed approach prevents over-fitting well. Furthermore, it is able to do automatic determination of the number of mixture components and parameters estimation, simultaneously. Experimental results on both synthetic and real data of object detection confirm significant improvements on flexibility and efficiency being achieved.
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