Massive amounts of videos are generated for entertainment, security,\u00a0and science, powered by a growing supply of user-produced\u00a0video hosting services. Unfortunately, searching for videos is difficult\u00a0due to the lack of content annotations. Recent breakthroughs in\u00a0image labeling with deep neural networks (DNNs) create a unique\u00a0opportunity to address this problem. While many automated end-to-end solutions have been developed, such as natural language\u00a0queries, we take on a different perspective: to leverage both the\u00a0development of algorithms and human capabilities. To this end, we design a query language in tandem with a user interface to help\u00a0users quickly identify segments of interest from the video based on\u00a0labels and corresponding bounding boxes. We combine techniques\u00a0from the database and information visualization communities to\u00a0help the user make sense of the object labels in spite of errors and\u00a0inconsistencies.<\/p>\n","protected":false},"excerpt":{"rendered":"
Massive amounts of videos are generated for entertainment, security,\u00a0and science, powered by a growing supply of user-produced\u00a0video hosting services. Unfortunately, searching for videos is difficult\u00a0due to the lack of content annotations. Recent breakthroughs in\u00a0image labeling with deep neural networks (DNNs) create a unique\u00a0opportunity to address this problem. While many automated end-to-end solutions have been developed, 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