ViTS: Video Tagging System from Massive Web Multimedia Collections

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI:10.1109/ICCVW.2017.48

Delia Fernandez, David Varas, Joan Espadaler, Issey Masuda, Jordi Ferreira, A. Woodward, David Rodriguez, Xavier Giró-i-Nieto, J. C. Riveiro, Elisenda Bou

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引用次数: 14

Abstract

The popularization of multimedia content on the Web has arised the need to automatically understand, index and retrieve it. In this paper we present ViTS, an automatic Video Tagging System which learns from videos, their web context and comments shared on social networks. ViTS analyses massive multimedia collections by Internet crawling, and maintains a knowledge base that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and linked with other related contents. ViTS is an industrial product under exploitation with a vocabulary of over 2.5M concepts, capable of indexing more than 150k videos per month. We compare the quality and completeness of our tags with respect to the ones in the YouTube-8M dataset, and we show how ViTS enhances the semantic annotation of the videos with a larger number of labels (10.04 tags/video), with an accuracy of 80,87%. Extracted tags and video summaries are publicly available.1

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ViTS:基于海量网络多媒体馆藏的视频标签系统

随着网络上多媒体内容的普及，产生了对多媒体内容自动理解、索引和检索的需求。在本文中，我们提出了一种自动视频标记系统ViTS，它可以从视频、网络背景和社交网络上分享的评论中学习。ViTS通过互联网爬行分析大量多媒体收藏，并维护一个知识库，该知识库无需人工监督即可实时更新。因此，每个视频都用一组丰富的标签进行索引，并与其他相关内容链接。ViTS是一个正在开发的工业产品，拥有超过250万个概念词汇表，每月能够索引超过15万个视频。我们将我们的标签的质量和完整性与YouTube-8M数据集中的标签进行了比较，并展示了ViTS如何增强具有更多标签(10.04个标签/视频)的视频的语义注释，准确率为80,87%。提取的标签和视频摘要是公开的

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2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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