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In this paper, we propose a novel mechanism of hierarchically indexing soccer video using visual, audio and textual cues. Firstly, video is indexed with information from pure video and audio respectively. Then, video is segmented into physical shots based on visual features, and then identified as syntactic shots according to broadcasting rules. Audio is analyzed to get physical contents and then...
Summary form only given. Techniques of computer and Internet are developing very fast, and users want to access their interested multimedia information from anywhere at anytime by using their most convenient digital equipments. Sports video always appeals to large audiences, and it becomes an important problem to automatically extracting useful semantic information from sports video to facilitate...
This paper presents a framework that explicitly detects events in broadcasting baseball videos and facilitates the development of various extended applications. Three phases are included: reliable shot classification, explicit event detection, and elaborate applications. In the shot classification stage, color and geometric information are utilized to classify shots into several canonical views. To...
Researchers worldwide have been actively seeking for the most robust and powerful solutions to detect and classify key events (or highlights) in various sports domains. Most approaches have employed manual heuristics that model the typical pattern of audio-visual features within particular sport events. To avoid manual observation and knowledge, machine-learning can be used as an alternative approach...
Sports videos have special characteristics such as well-defined video structure, specialized sports syntax, and typically having some canonical view types. In this paper, we propose a semi-supervised incremental learning framework for sports video view classification. Baseball is selected as an example to explain the main ideas. In order to obtain an optimal model based on a small number of pre-labeled...
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