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2017 | OriginalPaper | Chapter

Heterogeneous Multi-group Adaptation for Event Recognition in Consumer Videos

Authors : Mingyu Yao, Xinxiao Wu, Mei Chen, Yunde Jia

Published in: Image and Graphics

Publisher: Springer International Publishing

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Abstract

Event recognition in consumer videos has attracted much attention from researchers. However, it is a very challenging task since annotating numerous training samples is time consuming and labor expensive. In this paper, we take a large number of loosely labeled Web images and videos represented by different types of features from Google and YouTube as heterogeneous source domains, to conduct event recognition in consumer videos. We propose a heterogeneous multi-group adaptation method to partition loosely labeled Web images and videos into several semantic groups and find the optimal weight for each group. To learn an effective target classifier, a manifold regularization is introduced into the objective function of Support Vector Regression (SVR) with an \(\epsilon \)-insensitive loss. The objective function is alternatively solved by using standard quadratic programming and SVR solvers. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of our method.

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Metadata
Title
Heterogeneous Multi-group Adaptation for Event Recognition in Consumer Videos
Authors
Mingyu Yao
Xinxiao Wu
Mei Chen
Yunde Jia
Copyright Year
2017
Book
Image and Graphics

Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71607-7_51

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