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篇目详细内容 |
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【篇名】 |
Soft spectral clustering ensemble applied to image segmentation |
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【刊名】 |
Frontiers of Computer Science in China |
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【刊名缩写】 |
Front. Comput. Sci. China |
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【ISSN】 |
1673-7350 |
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【EISSN】 |
1673-7466 |
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【DOI】 |
10.1007/s11704-010-0161-9 |
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【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
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【出版年】 |
2011 |
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【卷期】 |
5
卷1期 |
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【页码】 |
66-78
页,共
13
页 |
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【作者】 |
Jianhua JIA;
Bingxiang LIU;
Licheng JIAO;
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【关键词】 |
spectral clustering (SC); Nystr鰉 approximation; centralized logcontrast transform; principal component analysis (PCA); ensemble learning |
【摘要】 |
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An unsupervised learning algorithm, named soft spectral clustering ensemble (SSCE), is proposed in this paper. Until now many proposed ensemble algorithms cannot be used on image data, even images of a mere 256 ？256 pixels are too expensive in computational cost and storage. The proposed method is suitable for performing image segmentation and can, to some degree, solve some open problems of spectral clustering (SC). In this paper, a random scaling parameter and Nystr鰉 approximation are applied to generate the individual spectral clusters for ensemble learning. We slightly modify the standard SC algorithm to aquire a soft partition and then map it via a centralized logcontrast transform to relax the constraint of probability data, the sum of which is one. All mapped data are concatenated to form the new features for each instance. Principal component analysis (PCA) is used to reduce the dimension of the new features. The final aggregated result can be achieved by clustering dimension-reduced data. Experimental results, on UCI data and different image types, show that the proposed algorithm is more efficient compared with some existing consensus functions. |
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