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In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve(More)
In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discirminant analysis framework. In MMDA, the within-class graph and between-class graph are respectively designed to characterize the within-class compactness and the(More)
This paper investigates the absolute exponential stability (AEST) of a class of neural networks with a general class of partially Lipschitz continuous and monotone increasing activation functions. The main obtained result is that if the interconnection matrix T of the neural system satisfies that T − is an H-matrix with nonnegative diagonal elements, then(More)
For better representation and feature extraction of face images, a two-stage method, called Sequential Row–Column 2DPCA (RC2DPCA), is proposed in this paper, which uses 2DPCA operated in the row direction and alternative 2DPCA operated in column direction. RC2DPCA can compress image in row and column direction. RC2DPCA needs fewer coefficients for image(More)