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Content Provider | IET Digital Library |
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Author | Zhang, Hui Wu, Qing Ming Jonathan Zheng, Yuhui Nguyen, Thanh Minh Wang, Dingcheng |
Abstract | Fuzzy c-means (FCMs) with spatial constraints have been considered as an effective algorithm for image segmentation. The well-known Gaussian mixture model (GMM) has also been regarded as a useful tool in several image segmentation applications. In this study, the authors propose a new algorithm to incorporate the merits of these two approaches and reveal some intrinsic relationships between them. In the authors model, the new objective function pays more attention on spatial constraints and adopts Gaussian distribution as the distance function. Thus, their model can degrade to the standard GMM as a special case. Our algorithm is fully free of the empirically pre-defined parameters that are used in traditional FCM methods to balance between robustness to noise and effectiveness of preserving the image sharpness and details. Furthermore, in their algorithm, the prior probability of an image pixel is influenced by the fuzzy memberships of pixels in its immediate neighbourhood to incorporate the local spatial information and intensity information. Finally, they utilise the mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. The mean template is considered as a spatial constraint for collecting more image spatial information. Compared with HMRF, their method is simple, easy and fast to implement. The performance of their proposed algorithm, compared with state-of-the-art technologies including extensions of possibilistic fuzzy c-means (PFCM), GMM, FCM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness. |
Starting Page | 571 |
Ending Page | 581 |
Page Count | 11 |
ISSN | 17519659 |
Volume Number | 8 |
e-ISSN | 17519667 |
Issue Number | Issue 10, Oct (2014) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/8/10 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2013.0178 |
Journal | IET Image Processing |
Publisher Date | 2014-05-01 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Bayes Method Bayesian Model Combinatorial Mathematics Computer Vision And Image Processing Technique Data Handling Technique Distance Function FCM Method Fuzzy C-means Clustering Algorithm Fuzzy Memberships Fuzzy Set Theory Gaussian Distribution Gaussian Mixture Model GMM Hidden Markov Random Field Model HMRF Model Image Pixel Image Segmentation Image Sharpness Image Spatial Information Immediate Neighbourhood Intensity Information Local Spatial Information Mean Template Objective Function Optical, Image And Video Signal Processing Pattern Clustering Prior Probability Prior Probability Estimation Probability Spatial Constraint Statistics |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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