An SVD-based Multimodal Clustering method for Social Event Detection

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

4 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - International Conference on Data Engineering
PublisherIEEE Computer Society
Pages202-209
Volume2015-June
ISBN (print)9781479984411
Publication statusPublished - 19 Jun 2015

Publication series

Name
Volume2015-June
ISSN (Print)1084-4627

Conference

Title2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015
PlaceKorea, Republic of
CitySeoul
Period13 - 17 April 2015

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.

Citation Format(s)

An SVD-based Multimodal Clustering method for Social Event Detection. / Ma, Yun; Li, Qing; Yang, Zhenguo et al.
Proceedings - International Conference on Data Engineering. Vol. 2015-June IEEE Computer Society, 2015. p. 202-209 7129577.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review