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Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which(More)
Obtaining robust and efficient rotation-invariant texture features in content-based image retrieval field is a challenging work. We propose three efficient rotation-invariant methods for texture image retrieval using copula model based in the domains of Gabor wavelet (GW) and circularly symmetric GW (CSGW). The proposed copula models use copula function to(More)
Conventional discriminant locality preserving projection (DLPP) is a dimensionality reduction technique based on manifold learning, which has demonstrated good performance in pattern recognition. However, because its objective function is based on the distance criterion using L2-norm, conventional DLPP is not robust to outliers which are present in many(More)
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