Compressive sensing(CS) has inspired significant interest because of its compressive capability and lack of complexity on the sensor side. In this paper, we present a study of three sampling patterns and investigate t...
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The Topic Models are a class of hierarchical statistical models for analyzing document collections and it has become one of the most used techniques in Natural Language Processing in the recent years. It assumes t...
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The Topic Models are a class of hierarchical statistical models for analyzing document collections and it has become one of the most used techniques in Natural Language Processing in the recent years. It assumes that each document could be expressed as a mixture of topics and each topic could be characterized by a distribution over words. In previous research [6], like in English language, Topic Models for Chinese Language use the words as observing data. In this research, we demonstrated the effectiveness of using Chinese characters as the basic units of observing data. The comparisons with those models based on Chinese words and English words are presented.
We present an efficient technique based on histogram evolution for summarizing video sequences to make them more amenable to browsing and retrieval. First, a ground-truth database of videos is generated in which the s...
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We present an efficient technique based on histogram evolution for summarizing video sequences to make them more amenable to browsing and retrieval. First, a ground-truth database of videos is generated in which the shot breaks are detected by human subjects and numbered in order. Three types of histogram are then used to capture the characteristics of color content containing in the video frames. The principle components analysis (PCA) method is adopted to reduce the histogram dimensions and form a 2D feature space. Finally, two approaches, frame difference measures and Fuzzy C-means clustering, are employed to extract video shot breaks. Polylines are drawn between the detected shot breaks to show that the histogram of their colors evolves from frame to frame. In comparison with the ground-truth database, the proposed algorithm achieves a surprising high detection accuracy rate. The extensive experiments also demonstrate that the patterns of histogram evolution can be useful to identify the shot break types, such as cut, dissolve, fade-out, fade-in, and wipe.
For the time series prediction problem, the relationship between the abstracted independent variables and the response variable is usually strong non-linear. We propose a neural network fusion model based on k-hyperpl...
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For the time series prediction problem, the relationship between the abstracted independent variables and the response variable is usually strong non-linear. We propose a neural network fusion model based on k-hyperplanes for non-linear regression. A k-hyperplane clustering algorithm is developed to split the data to several clusters. The experiments are done on an artificial time series, and the convergence of k-hyperplane clustering algorithm and neural network gradient training algorithm is examined. The dimension of inputs affect the clustering performance very much. Neural network fusion can get some compensation in performance. It is shown that the prediction performance of the model for the time series is very good. The model can be further exploited for many real applications.
Support Vector machine (SVM) is a classification technique of machinelearning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the s...
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