NDLI: Searching informative concept banks for video event detection

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Searching informative concept banks for video event detection

Content Provider	ACM Digital Library
Author	Snoek, Cees Gavves, Efstratios van de Sande, Koen Mazloom, Masoud
Abstract	An emerging trend in video event detection is to learn an event from a bank of concept detector scores. Different from existing work, which simply relies on a bank containing all available detectors, we propose in this paper an algorithm that learns from examples what concepts in a bank are most informative per event. We model finding this bank of informative concepts out of a large set of concept detectors as a rare event search. Our proposed approximate solution finds the optimal concept bank using a cross-entropy optimization. We study the behavior of video event detection based on a bank of informative concepts by performing three experiments on more than 1,000 hours of arbitrary internet video from the TRECVID multimedia event detection task. Starting from a concept bank of 1,346 detectors we show that 1.)some concept banks are more informative than others for specific events, 2.) event detection using an automatically obtained informative concept bank is more robust than using all available concepts, 3.) even for small amounts of training examples an informative concept bank outperforms a full bank and a bag-of-word event representation, and 4.) we show qualitatively that the informative concept banks make sense for the events of interest, without being programmed to do so. We conclude that for concept banks it pays to be informative.
Starting Page	255
Ending Page	262
Page Count	8
File Format	PDF
ISBN	9781450320337
DOI	10.1145/2461466.2461507
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cross-entropy optimization Event recognition Concept detection
Content Type	Text
Resource Type	Article

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