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Image Segmentation by Student ' s-t Mixture Models Based on Markov Random Field and Weighted Mean Template
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhu, Hongqing Xie, Qunyi |
| Copyright Year | 2016 |
| Abstract | Finite mixture model (FMM) with Gaussian distribution has been widely used in many image processing and pattern recognition tasks. This paper presents a new Student's-t mixture model (SMM) based on Markov random field (MRF) and weighted mean template. In this model, the Student's-t distribution is considered as an alternative to the Gaussian distribution due to the former is heavily tailed than Gaussian distribution, thus providing robustness to outliers. With the help of the weighted mean template, the spatial information between neighboring pixels of an image is considered during the learning step. In addition, the proposed method is able to impose the smoothness constraint on the pixel label by using MRF. Furthermore, an efficient energy function and a novel factor are applied in current model to decrease the computational complexity. Numerical experiments are presented on simulated and real world images, and the results are compared with other FMM-based models. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.sersc.org/journals/IJSIP/vol9_no2/27.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Clinical Use Template Cluster analysis Computational complexity theory Electron Microscopy Expectation–maximization algorithm Experiment Fast multipole method Fuzzy clustering Grayscale Color Map Image processing Image segmentation Loss function Markov chain Markov random field Mathematical optimization Mixture model Normal Statistical Distribution Numerical method Optimization problem Pattern recognition Pixel Simulation Tail biologic segmentation lapatinib statistical cluster |
| Content Type | Text |
| Resource Type | Article |
