A Fuzzy Kernel Maximum Margin Criterion for Image Feature Extraction
Based on kernel principal component analysis, fuzzy set theory, and maximum margin criterion, a novel image feature extraction and recognition method, called fuzzy kernel maximum margin criterion (FKMMC), is proposed. In the proposed method, two new fuzzy scatter matrixes are redefined. The new fuzz...
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2015-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/641510 |
| Summary: | Based on kernel principal component analysis, fuzzy set theory, and maximum margin criterion, a novel image feature extraction and recognition method, called fuzzy kernel maximum margin criterion (FKMMC), is proposed. In the proposed method, two new fuzzy scatter matrixes are redefined. The new fuzzy scatter matrix can reflect fully the relation between fuzzy membership degree and the offset of the training sample to subclass center. Besides, a concise reliable computational method of the fuzzy between-class scatter matrix is provided. Experimental results on four face databases (AR, extended Yale B, GTFD, and FERET) demonstrate that the proposed method outperforms other methods. |
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| ISSN: | 1024-123X 1563-5147 |