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Web-scale Multimedia Search for Internet Video Content

The World Wide Web has been witnessing an explosion of video content. Video data are becoming one of the most valuable sources to assess insights and information. However, existing video search methods are still based on text matching (text-to-text search), and could fail for the huge volumes of videos that have little relevant metadata or no metadata at all. In this paper, we propose an accurate, efficient and scalable semantic search method for Internet videos that allows for intelligent and flexible search schemes over the video content (text-to-video search and text&videoto-video search). To achieve this ambitious goal, we propose several novel methods to improve accuracy and efficiency. The extensive experiments demonstrate that the proposed methods are able to surpass state-of-the-art accuracy and ef- ficiency on multiple datasets. Based on the proposed methods, we implement E-Lamp Lite, the first of its kind largescale semantic search engine for Internet videos. According to National Institute of Standards and Technology (NIST), it achieved the best accuracy in the TRECVID Multimedia Event Detection (MED) 2013, 2014 and 2015, one of the most representative task for content-based video search. To the best of our knowledge, E-Lamp Lite is the first contentbased semantic search system that is capable of indexing and searching a collection of 100 million videos.

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