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• W2545602975 abstract "Video retrieval has been a hot topic due to the prevalence of video capturing devices and media-sharing services such as YouTube. Until now, few past studies has focused on querying the videos by images due to the semantic gap between images and videos is not easy to narrow. To this end, in this paper, we propose a novel semantic video retrieval system that integrates web image annotation and concept matching function to bridge images, concepts and videos. For web image annotation, we exploit textual and visual information in the web image to achieve effective image annotation. For concept matching function, we identify the concept relations by calculating the similarity between two concepts via Word Net. On the basis of web image annotation and concept matching function, the proposed system reaches the goals of usability and intelligence on semantic video retrieval. The experimental results reveal that our proposed system can successfully capture the user's intention between image concepts and video concepts for semantic video retrieval." @default.
• W2545602975 created "2016-11-04" @default.
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• W2545602975 date "2011-11-01" @default.
• W2545602975 modified "2023-10-16" @default.
• W2545602975 title "Semantic Video Retrieval by Integrating Concept- and Content-Aware Mining" @default.
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• W2545602975 doi "https://doi.org/10.1109/taai.2011.14" @default.
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