NewsBR: a content-based news video and retrieval system
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An advanced content-based news video browsing and retrieval system, NewsBR, is proposed in this work. The system is built on high-accuracy news story segmentation and topic caption text extraction. Its main features include category-based news story browsing, key-frame-based video abstract and keyword-based news story retrieval. In the paper, news story segmentation and topic caption text extraction, as well as content-based video browsing and retrieval, are addressed in detail. The system is helpful and effective for the overall understanding of the news video content.Keywords:
Video retrieval
Key frame
Video browsing
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Key-frames extraction is a very important step in the organization of content-based video retrieval and browsing. In the whole procedure of the organization and studying in the video database, key-frames extraction is always neglected because of the limitation of the computer's ability in processing. This paper discusses on some new ways in allusion to key-frames extraction of video's characters in video database indexing. Adaptive time algorithm are proposed. The video database experiments are used to prove the effectiveness of the proposed method. It also presents an integrated system solution for computer-assisted video parsing and content-based video retrieval.
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A key-frame should be efficiently selected to distinguish bad information from the videos. A previous technique selecting a key-frame mostly consists of the transformation scene-centered. In the case of harmful videos containing the quaility of continuous changes, the technique makes the total rate be reduced by an unnecessary key-frame. This thesis suggests the technique selecting a key-frame, an entry of the distinguishing system by using the quality of changes between the frames. In the experiment of this technique, it was proved that over 90% of the bad information was distinguished by the selected key frame, and also time efficiency was proved by showing 68% of decrement compared to the numbers I-frame. Therefore, This technique makes the system efficient to distinguish bad information, and efficiently can contribute to the distribution of the healthy movie information.
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This paper has analyzed the Insufficiency and trend of current research progress of key techniques of content-based video retrieval,which beside the shot boundary detection,key frame extraction,shot cluster and the scene extraction and the video frequency index and the browsing and so on.
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Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.
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the multimedia storage grows and the cost for storing multimedia data is cheaper. So there is huge number of videos available in the video repositories. It is difficult to retrieve the relevant videos from large video repository as per user interest as users shift from text based retrieval systems to content based retrieval systems. Video retrieval is very important in multimedia database management. This paper offers an overview of the landscape of general strategies in visual content-based video retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction, extraction of features including static key frame features, object feature, video retrieval including similarity measures and the proposed procedure consists of the unique aspect of clustering techniques.
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With the development of computer technology, teaching video data grows exponentially. How to rapidly capture the wanted video data at the sea video database is the currently hot research topic. This paper put forward to a method of key frame extraction based on mutual information for teaching content-based video analysis and retrieval. Experiments show that this proposed approach can make a good representation for video contents.
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Research on how to efficiently access video content has become increasingly active in the past few years. Considerable progress has been made in video analysis, representation, browsing, and retrieval, which are the four fundamental bases for accessing video content. Video analysis deals with the signal processing part of the video system, including shot boundary detection, key frame extraction, etc. Video representation is concerned with the structure of the video. An example of a video representation is the tree structured key frame hierarchy. Built on top of the video representation, video browsing deals with how to use the representation structure to help viewers browse the video content. Finally, video retrieval is concerned with retrieving interesting video objects.
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