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Human action recognition is a challenging vision task due to the complex action patterns in the real-world videos. In this work, we propose a DeepAction Kernel Gaussian Process, which takes advantage of Gaussian process (GP) and deep learning, to capture the distinctive action characteristics. Specifically, we design a unified, deep and non-adjacent kernel structure within Gaussian process to classify...
Voice conversion can be reduced to a problem to find a transformation function between the corresponding speech sequences of two speakers. Perhaps the most voice conversions methods are GMM-based statistical mapping methods. However, the classical GMM-based mapping is frame-to-frame, and cannot take account of the contextual information existing over a speech sequence. It is well known that HMM yields...
Identifying features invariant to certain transformations is a fundamental problem in the fields of signal processing and pattern recognition. This correspondence explores a family of measures called f-divergences that are invariant to invertible transformations, and studies their application to speech recognition. We provide novel proofs for the sufficiency and necessity of the invariance of f-divergence...
In this paper, a novel gradient Gabor (GGabor) filter is proposed to extract multi-scale and multi-orientation features to represent and classify faces. Gradient Gabor combines the derivative of Gaussian functions and the harmonic functions to capture the features in both spatial and frequency domains to deliver orientation and scale information. The spatial positions are combined into Gaussian derivatives...
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