Our successful multimedia event detection system at TRECVID 2015 showed its strength on handling complex concepts in a query. The system was based on a large number of pre-trained concept detectors for textual-to-visual relation. In this paper, we enhance the system by enabling human-in-the-loop. In order to facilitate a user to quickly find an information need, we incorporate concept screening, video reranking by highlighted concepts, relevance feedback and color sketch to refine a coarse retrieval result. The aim is to eventually come up with a system suitable for both Ad-hoc Video Search and Known-Item Search. In addition, as the increasing awareness of difficulty in distinguishing shots of very similar scenes, we also explore the automatic story annotation along the timeline of a video, so that a user can quickly grasp the story happened in the context of a target shot and reject shots with incorrect context. With the story annotation, a user can refine the search result as well by simply adding a few keywords in a special “context field” of a query.
CITATION STYLE
Lu, Y. J., Nguyen, P. A., Zhang, H., & Ngo, C. W. (2017). Concept-based interactive search system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10133 LNCS, pp. 463–468). Springer Verlag. https://doi.org/10.1007/978-3-319-51814-5_42
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