Double Gaussian mixture model for image segmentation with spatial relationships |
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| Affiliation: | 1. College of Applied Mathematics, Chengdu University of Information Technology, Chengdu 610225, Sichuan, PR China;2. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, PR China;1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China;2. School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China;3. SYSU-CMU Shunde International Joint Research Institute, Shunde, Guangdong, China;4. Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510006, China;1. School of Bio-information, Chongqing University of Posts and Telecommunications, Chongqing, China;2. Department of Computer Science, The University of Central Arkansas, Conway, USA;3. Department of Network Engineering, Chengdu University of Information Technology, Chengdu, China;4. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;5. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;1. School of Digital Media, Jiangnan University, Wuxi 214122, China;2. Faculty of Information Technology, Macau University of Science and Technology, Macau;1. School of Computer, Central China Normal University, Wuhan 430079, PR China;2. Département de Physique, École Normale Supérieure, 24, rue Lhomond, 75231 Paris Cedex 5, France |
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| Abstract: | In this paper, we present a finite mixture model based on a Gaussian distribution for image segmentation. There are four advantages to the proposed model. First, compared with the standard Gaussian mixture model (GMM), the proposed model effectively incorporates spatially relationships between the pixels using a Markov random field (MRF). Second, the proposed model is similar to GMM, but has a simple representation and is easier to implement than some existing models based on MRF. Third, the contextual mixing proportion of the proposed model is explicitly modelled as a probabilistic vector and can be obtained directly during the inference process. Finally, the expectation maximization algorithm and gradient descent approach are used to maximize the log-likelihood function and infer the unknown parameters of the proposed model. The performance of the proposed model at image segmentation is compared with some state-of-the-art models on various synthetic noisy grayscale images and real-world color images. |
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| Keywords: | Markov random model Gaussian mixture model Image segmentation Expectation maximization (EM) algorithm Gradient descent Spatial relationships Synthetic noisy grayscale images Real-world color images |
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