Enhanced fisher discriminant criterion for image recognition
Many previous studies have shown that image recognition can be significantly improved by Fisher linear discriminant analysis (FLDA) technique. However, FLDA ignores the variation of data points from the same class, which characterizes the most important modes of variability of patterns and helps to...
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Published in | Pattern recognition Vol. 45; no. 10; pp. 3717 - 3724 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Kidlington
Elsevier Ltd
01.10.2012
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | Many previous studies have shown that image recognition can be significantly improved by Fisher linear discriminant analysis (FLDA) technique. However, FLDA ignores the variation of data points from the same class, which characterizes the most important modes of variability of patterns and helps to improve the generalization capability of FLDA. Thus, the performance of FLDA on testing data is not good enough. In this paper, we propose an enhanced fisher discriminant criterion (EFDC). EFDC explicitly considers the intra-class variation and incorporates the intra-class variation into the Fisher discriminant criterion to build a robust and efficient dimensionality reduction function. EFDC obtains a subspace which best detects the discriminant structure and simultaneously preserves the modes of variability of patterns, which will result in stable intraclass representation. Experimental results on four image database show a clear improvement over the results of FLDA-based methods.
► A new graph is introduced to encode the most important modes of variability of patterns. ► We employ a scatter matrix, called variability scatter matrix, to measure the variability of patterns. ► A linear approach is presented based on the variability scatter matrix, between-class and within-class scatter matrices. ► The proposed approach well detect the discriminant structure and simultaneously preserves the intrinsic geometric of patterns. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0031-3203 1873-5142 |
DOI: | 10.1016/j.patcog.2012.03.024 |