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In this paper, an effective synthetic aperture radar image segmentation method is proposed. Gaussian mixture models optimized by greedy expectation maximization algorithm are applied. The immune genetic algorithm is employed to initialize greedy expectation maximization algorithm and search the optimal values in the whole range, instead of general k-means algorithm, which is different from the traditional...
Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms...
Trace transform is an alternative representation of original image, and various transforms can be obtained by implementing different Trace functionals. Taking the advantages of diversified functionals, ensemble Trace transforms based face recognition system is proposed in which majority voting plays the role of decision making. In addition, weighted majority voting and genetic algorithms are combined...
The hybrid trace features (HTF), a new face representation, is proposed for face authentication system. Trace transforms of multiple Trace functionals are used to construct the HTF, and Genetic Algorithms is implemented as the data fusion tool. In addition, rotation based hybrid trace features (r-HTF) is also introduced as facial features. The systemic evaluations on Cambridge ORL face database reveal...
Principal component analysis (PCA) is one of the most traditional linear dimensionality reduction algorithms. Kernel principal component analysis (kernel PCA), generalization of PCA, is a nonlinear feature extraction method. However, both PCA and kernel PCA are lack of class information in their feature subspace. In this paper, weighted kernel principal component analysis (WKPCA) is proposed for feature...
Principal component analysis (PCA) and linear discriminant analysis (LDA) are two commonly used feature extraction techniques. In this paper, a nonlinear evolutionary weighted principal component analysis (EWPCA) based on genetic algorithms is proposed. Similar to LDA, the EWPCA maximizes the ratio of between-class variations to that of within-class variations, and achieves better classification performance...
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