An SVD-based Multimodal Clustering method for Social Event Detection

Yun Ma, Qing Li, Zhenguo Yang, Zheng Lu, Haiwei Pan, Antoni B. Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

With the rapid development of social media sites such as Flickr, user-generated multimedia content on the Web has shown an explosive growth in recent years. Social event detection from these large multimedia collections has become one of the hottest topics in analysis of Web content. In this paper, an SVD-based Multimodal Clustering (SVDMC) algorithm is proposed to detect social events from multimodal data. SVDMC is a completely unsupervised approach aiming to take full advantage of the data at hand. Through using the binary adjacency matrix and Singular Value Decomposition (SVD), SVDMC is robust to data incompleteness for datasets in real world. Experiments conducted on the MediaEval Social Event Detection (SED) 2012 dataset demonstrate the effectiveness of the proposed method as well as discriminative power of different features.

Original languageEnglish
Title of host publicationICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops
PublisherIEEE Computer Society
Pages202-209
Number of pages8
ISBN (Electronic)9781479984411
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015 - Seoul, Korea, Republic of
Duration: 13 Apr 201517 Apr 2015

Publication series

NameProceedings - International Conference on Data Engineering
Volume2015-June
ISSN (Print)1084-4627

Conference

Conference2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015
CountryKorea, Republic of
CitySeoul
Period13/04/1517/04/15

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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