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In this paper, we propose a novel method to extract keyframes from motion capture data for people to better visualize and understand the content of the motion. It first applies a Butterworth filter to remove the noise in the motion capture data, then carries out principal component analysis (PCA) to reduce the dimension. By detecting the zero-crossing points of the velocity in the principal components,...
Video summary technology has become a hotspot of current researches. The application of sports video summary can quickly fetch important information in sports video that help sports enthusiasts and sports senior analysis the video. The present study takes tennis video as the research object. Firstly, determine the number of key frames based on statistical rules, and then extract key frames from different...
Aimed at solving the problem that traditional clustering methods are vulnerable to the sparsity feature of the high dimensional data, a spectral clustering algorithm is proposed based on K-SVD dictionary learning. The algorithm firstly learns a dictionary by K-SVD and obtains sparse representation coefficients of all data samples in the dictionary by l1 sparse optimization. Then the similarity matrix...
Social networks have become the most important source of news and people's feedback and opinion about almost every daily topic. With this massive amount of information over the web from different social networks like Twitter, Facebook, Blogs, etc, there has to be an automatic tool that can determine the topics that people are talking about and what are there sentiments about these topics. The goal...
Because of a large number of micro-blog's junk posts, how to instantly browse hot topics in micro-blog is faced with severe challenges. We propose a new extraction algorithm of hot topics to remove the false hot topics. The algorithm sets a group of parameters what is a determine condition. Parameters contain word frequency, users' number, occurrence number, users' discrete degrees, and time distribution...
This paper first studies the methods of web documents mining and text clustering, and summaries the fuzzy clustering algorithms and similarity measure functions, then proposes a modified similarity function which can solve the problems of feature selection and feature extraction in high-dimensional space. Finally, this paper puts forward to a dynamic fluzzy clustering algorithm(DCFCM) by combining...
In this paper, we focus on a single graph whose vertices contain a set of quantitative attributes. Several networks can be naturally represented in this complex graph. An example is a social network whose vertex corresponds to a person with some quantitative items such as age, salary and so on. Although it can be expected that this kind of data will increase rapidly, most of current graph mining algorithms...
This paper presents PDF-TREX, an heuristic approach for table recognition and extraction from PDF documents.The heuristics starts from an initial set of basic content elements and aligns and groups them, in bottom-up way by considering only their spatial features, in order to identify tabular arrangements of information. The scope of the approach is to recognize tables contained in PDF documents as...
A network model using Self-organizing map (SOM) and Outputs Modifiable Radial Basis Function (OMRBF) is proposed to identify acoustic fault of underwater vehicles. This model integrates unsupervised SOM with supervised OMRBF to accomplish incremental learning. The outputs neurons of this model can be modified on-line, and SOM is utilized to determine the optimal number of hidden neurons. Experiment...
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