Double Gaussian mixture model for image segmentation with spatial relationships

作者：Xiong, Taisong; Zhang, Lei^*; Yi, Zhang

来源：Journal of Visual Communication and Image Representation, 2016, 34: 135-145.

DOI：10.1016/j.jvcir.2015.10.018

• Markov random model
• Gaussian mixture model
• Image segmentation
• Expectation maximization (EM) algorithm
• Gradient descent
• Spatial relationships
• Synthetic noisy grayscale images
• Real-world color images

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.

• 出版日期2016-1
• 单位四川大学; 成都信息工程大学