Automatic Preview Frame Selection for Online Videos

Zhang, B; Wang, Z; Tao, D; Hua, XS; Feng, DD

Automatic Preview Frame Selection for Online Videos

Zhang, B Wang, Z Tao, D

Hua, XS Feng, DD

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Publication Type:: Conference Proceeding
Citation:: 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015, 2015
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Zhang, B	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Hua, XS	en_US
dc.contributor.author	Feng, DD	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015, 2015	en_US
dc.identifier.isbn	9781467367950	en_US
dc.identifier.uri	http://hdl.handle.net/10453/94123
dc.description.abstract	© 2015 IEEE. The preview frame of an online video plays a critical role for a user to quickly decide whether to watch the video. However, the preview frames of most online videos such as those shared on social media platforms are either selected heuristically (e.g., the first or middle frame of a video) or manually by users or experienced editors. In this paper, we investigate the challenging automatic preview frame selection task and formulae it as a classification problem. To our best knowledge, this is the one of the first attempts on this topic, since most existing key frame selection methods do not explicitly aim for selecting the best representative one only. Considering that a preview frame for an entire video should be informative in the context of the video story, attention catching, and of high visual quality, we propose three types of features to characterize each video frame: informativeness, attention, and aesthetics. Due to the imbalanced nature of training data (i.e., one preview frame only vs thousands of non-preview frames in a video), we utilize random forests to learn the features of preview frames and to classify each frame into preview frame or non-preview frame. In addition, we also increase the number of positive training samples by identifying frames which are visually similar to the preview frame. We evaluated our proposed method both quantitatively and qualitatively with a set of 180 news videos manually collected from the BBC news website. Experimental results indicate that our method is promising. We also investigated the contribution of each visual feature to guide future studies.	en_US
dc.relation.ispartof	2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015	en_US
dc.relation.isbasedon	10.1109/DICTA.2015.7371237	en_US
dc.title	Automatic Preview Frame Selection for Online Videos	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. The preview frame of an online video plays a critical role for a user to quickly decide whether to watch the video. However, the preview frames of most online videos such as those shared on social media platforms are either selected heuristically (e.g., the first or middle frame of a video) or manually by users or experienced editors. In this paper, we investigate the challenging automatic preview frame selection task and formulae it as a classification problem. To our best knowledge, this is the one of the first attempts on this topic, since most existing key frame selection methods do not explicitly aim for selecting the best representative one only. Considering that a preview frame for an entire video should be informative in the context of the video story, attention catching, and of high visual quality, we propose three types of features to characterize each video frame: informativeness, attention, and aesthetics. Due to the imbalanced nature of training data (i.e., one preview frame only vs thousands of non-preview frames in a video), we utilize random forests to learn the features of preview frames and to classify each frame into preview frame or non-preview frame. In addition, we also increase the number of positive training samples by identifying frames which are visually similar to the preview frame. We evaluated our proposed method both quantitatively and qualitatively with a set of 180 news videos manually collected from the BBC news website. Experimental results indicate that our method is promising. We also investigated the contribution of each visual feature to guide future studies.

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http://hdl.handle.net/10453/94123