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Video Retrieval by Learning Uncertainties in Concept Detection from Imbalanced Annotation Data

Authors:
Kenji Kumabuchi
Kimiaki Shirahama
Kuniaki Uehara

Keywords: Video retrieval; Uncertainty in concept detection; Dempster-Shafer theory; Imbalanced problem; Density ratio;

Abstract:
Concept-based video retrieval retrieves shots relevant to a query based on detection results of concepts, such as Person, Building and Car. However, concept detection is ‘uncertain’ because even state-of-the-art methods cannot accurately detect various concepts. Thus, we introduce a video retrieval method, which models the uncertainty in the detection of each concept using ‘plausibilities’. A plausibility represents an upper bound of probability that the concept is present (or absent) in a shot. Using such plausibilties, false positive and false negative detections of the concept can be effectively managed. We derive plausibilities by estimating the density ratio between shots annotated with the concept’s presence and absence. However, annotating randomly sampled shots does not lead appropriate plausibilities due to the ‘imbalanced problem’. This means that the number of shots where the concept is present is generally much smaller than the number of shots where it is absent. To overcome this, a selective sampling method is developed to　preferentially sample unannotated shots, which are similar to shots already annotated with the concept’s presence. Experimental results on TRECVID 2009 video data validates the effectiveness of derived plausibilities.

Pages: 19 to 24

Copyright: Copyright (c) IARIA, 2013

Publication date: April 21, 2013

Published in: conference

ISSN: 2308-4448

ISBN: 978-1-61208-265-3

Location: Venice, Italy

Dates: from April 21, 2013 to April 26, 2013