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Content Provider | IEEE Xplore Digital Library |
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Author | Bingxiang Liu Jianhua Jia |
Copyright Year | 2011 |
Description | Author affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, China (Bingxiang Liu; Jianhua Jia) |
Abstract | Image segmentation is a fundamental problem in computer vision. Recently, ensemble learning receives more and more attention for its robustness, novelty and stability. Generally there are two problems in ensemble learning. One is the generation of the individuals of ensemble. The other is the consensus function of the individuals. We focus on the second problem. A new consensus function is proposed for texture images segmentation. To the consensus function, the spatial information of image, that means the adjacent pixels belong to the same class with a high probability, are considered via MRF. Expectation Maximum (EM) algorithm is applied to estimate the parameters of the model and converges fast. The experimental results show that the performance of our model is better than SC using Nyström method and the SCE via mixture model proposed by Topchy for image segmentation. |
Starting Page | 550 |
Ending Page | 554 |
File Size | 207838 |
Page Count | 5 |
File Format | |
ISBN | 9781424487271 |
e-ISBN | 9781424487288 |
DOI | 10.1109/CSAE.2011.5953280 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-06-10 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Image segmentation Unsupervised ensemble Computational modeling Markov Random Model (MRF) Clustering algorithms Feature extraction Spectral clustering Pattern analysis Pixel |
Content Type | Text |
Resource Type | Article |
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