@inproceedings{80f7c2c160234d4184192da13bac2d90,
title = "Heterogeneous multi-group adaptation for event recognition in consumer videos",
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 ϵ -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.",
keywords = "Event recognition, Multi-group adaptation, Transferring learning",
author = "Mingyu Yao and Xinxiao Wu and Mei Chen and Yunde Jia",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 9th International Conference on Image and Graphics, ICIG 2017 ; Conference date: 13-09-2017 Through 15-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71607-7_51",
language = "English",
isbn = "9783319716060",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "577--589",
editor = "Yao Zhao and David Taubman and Xiangwei Kong",
booktitle = "Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers",
address = "Germany",
}