Abstract
We present the most recent version of our Deep Interactive Video Exploration (diveXplore) system, which has been successfully used for the latest two Video Browser Showdown competitions (VBS2017 and VBS2018) as well as for the first Lifelog Search Challenge (LSC2018). diveXplore is based on a plethora of video content analysis and processing methods, such as simple color, texture, and motion analysis, self-organizing feature maps, and semantic concept extraction with different deep convolutional neural networks. The biggest strength of the system, however, is that it provides a variety of video search and rich interaction features. One of the novelties in the most recent version is a Feature Map Autopilot, which ensures time-efficient inspection of feature maps without gaps and unnecessary visits.
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Schoeffmann, K., Münzer, B., Leibetseder, A., Primus, J., Kletz, S. (2019). Autopiloting Feature Maps: The Deep Interactive Video Exploration (diveXplore) System at VBS2019. 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_50
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DOI: https://doi.org/10.1007/978-3-030-05716-9_50
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