Abstract
In this paper, the VIREO team video retrieval tool is described in details. As learned from Video Browser Showdown (VBS) 2018, the visualization of video frames is a critical need to improve the browsing effectiveness. Based on this observation, a hierarchical structure that represents the video frame clusters has been built automatically using k-means and self-organizing-map and used for visualization. Also, the relevance feedback module which relies on real-time support-vector-machine classification becomes unfeasible with the large dataset provided in VBS 2019 and has been replaced by a browsing module with pre-calculated nearest neighbors. The preliminary user study results on IACC.3 dataset show that these modules are able to improve the retrieval accuracy and efficiency in real-time video search system.
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Acknowledgments
The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong SAR, China (Reference No.: CityU 11250716), and a grant from the PROCORE-France/Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the Consulate General of France in Hong Kong (Reference No.: F-CityU104/17).
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Nguyen, P.A., Ngo, CW., Francis, D., Huet, B. (2019). VIREO @ Video Browser Showdown 2019. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_54
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