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A Deep Siamese Network for Scene Detection in Broadcast Videos

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type , Conference object 13 Oct 2015Embargo end date: 01 Jan 2015 Italy Publisher:ACMJournal:Proceedings of the 23rd ACM international conference on Multimedia

Authors: BARALDI, LORENZO; GRANA, Costantino; CUCCHIARA, Rita;

doi: 10.1145/2733373.2806316 , 10.48550/arxiv.1510.08893

arXiv: http://arxiv.org/abs/1510.08893

handle: 11380/1074327

A Deep Siamese Network for Scene Detection in Broadcast Videos

- Summary
- Subjects
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Abstract

We present a model that automatically divides broadcast videos into coherent scenes by learning a distance measure between shots. Experiments are performed to demonstrate the effectiveness of our approach by comparing our algorithm against recent proposals for automatic scene segmentation. We also propose an improved performance measure that aims to reduce the gap between numerical evaluation and expected results, and propose and release a new benchmark dataset.

ACM Multimedia 2015

Country

Italy

Related Organizations

University of Modena and Reggio Emilia
Italy

Keywords

FOS: Computer and information sciences, Deep Learning, Scene Segmentation, Video Re-use, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia, Multimedia (cs.MM)

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	91
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%