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In this paper, we propose a novel image representation for scene classification. Firstly, we model multiple order statistics of image patches via Gaussian Mixture Model(GMM) in a Bayesian framework. Secondly, we combine the information of mean and covariance of the GMM and represent it as a mean-covariance supervector through a new distance metric. Experimental results demonstrate that our new representation,...
While a sound spoken is described by a handful of frame-level spectral vectors, not all frames have equal contribution for either human perception or machine classification. In this paper, we introduce a novel framework to automatically emphasize important speech frames relevant to phonetic information. We jointly learn the importance of speech frames by a distance metric across the phone classes,...
Patch-based approaches have become popular in many computer vision applications over recent years. An intrinsic flaw of this framework, missing of the spatial information, however, restricts its usage in face related applications where the spatial configuration is relatively settled. In this paper, we introduce a new patch feature representation, namely spatial Gaussian mixture models (SGMM), which...
The term structure models of interest rate have been extensively applied to asset pricing, design of financial instruments, hedging, arbitraging and investment decision. Therefore, the estimation of parameters in the term structure model has been a key problem. In this paper, the parameters of term structure model is estimated by using two different kernel functions: Gauss kernel function and Epanechnikov...
In this paper, we present a patch-based regression framework for addressing the human age and head pose estimation problems. Firstly, each image is encoded as an ensemble of orderless coordinate patches, the global distribution of which is described by Gaussian mixture models (GMM), and then each image is further expressed as a specific distribution model by Maximum a Posteriori adaptation from the...
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