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We propose a discriminative feature selection method utilizing support vector machines for the challenging task of multiview face recognition. According to the statistical relationship between the two tasks, feature selection and multiclass classification, we integrate the two tasks into a single consistent framework and effectively realize the goal of(More)
According to statistical learning theory, we propose a feature selection method using support vector machines (SVMs). By exploiting the power of SVMs, we integrate the two tasks, feature selection and classifier training, into a single consistent framework and make the feature selection process more effective. Our experiments show that our SVM feature(More)
In recent years, many document image retrieval algorithms have been proposed. However, most of the current approaches either need good quality images or depend on the page layout structure. This paper presents a fast, accurate and OCR-free image retrieval algorithm using local feature sequences which can describe the intrinsic, unique and page-layout-free(More)
In this paper, we present local patterns constrained image histograms (LPCIH) for efficient image retrieval. Extracting information through combining local texture patterns with global image histogram, LPCIH is an effective image feature representation method with a flexible image segmentation process. This kind of feature representation is robust and(More)
By combining the two standard paradigms of unsupervised learning, Principal Component Analysis (PCA) and Gaussian density estimation, this paper proposes an adjusted Gaussian skin-color model for skin-color detection. This method is more robust than the standard Gaussian model because it can weaken the bias caused by noise and enhance the fitness of the(More)
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