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Topic Detection is a sub-task of Topic Detection and Tracking, its main task is to find and organize topics that system didn't know. By analyzing hundreds of website news reports, we find that usually there exist some keywords in text, and early study didn't pay enough attention to this, we propose a topic detection algorithm according this. The algorithm is based on K-means clustering algorithm,...
Through the Study of English semantic similarity, based on the previous, we designed the method of calculating English semantic similarity by Word Net. We proposed a vector space representation model of English standard information based on semantic similarity and applied the model on text clustering. This model resolves the problem of semantic correlation between the characteristics which is ignored...
Through research on K-means algorithm of text clustering and semantic-based vector space model, a semantic-based K-means text clustering model is proposed to solve the problem on high-dimensional and sparse characteristics of text data set. The model reduces the semantic loss of the text data and improves the quality of text clustering. Experiments prove that semantic-based text clustering increases...
Information deficiency is a huge problem when researching on video indexing and retrieval. On the other hand, text in video frames implies lots of semantics inherently, and can provide supplemental but important information for video data processing. A smart approach for text detection, localization and extraction in video frames is presented in this paper. Here, block change rate (BCR for short)...
Information deficiency is a huge problem when researching on video indexing and retrieval. On the other hand, text in video frames implies lots of semantics inherently, and can provide supplemental but important information for video data processing. In this paper, we present a fast and robust approach for text detection, localization, extraction, and reorganization in video frames with complex background...
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