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Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DTs) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint...
Recently, sparse representation (SR) over a redundant dictionary has become a popular way of representing the data. It has been verified as an efficient and useful tool to promote the discrimination between signals. This work develops a joint learning approach to find the low dimensional discriminative features for high dimensional data. To avoid the high computational cost of direct sparse coding...
The problem of multidimensional data reconstruction from an incomplete set of observations is addressed in this paper. It has been recently shown that learned dictionaries are very effective in image denoising and inpainting applications. Here we extend the core idea in image inpainting to the case of 3-D data. Our main objective is to exploit both spatial and spectral/temporal information for recovering...
This paper firstly uses Baidu Baike and co-occurrence proportion of adjacent words after sentence segmentation to identify new words to decreases the effect on recognition of feature because of segmentation errors. We design part of speech sequence patterns to obtain candidate feature word set from Chinese product comments, then utilize a series of effective statistical technique and natural language...
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