Semi-supervised Multimodal Clustering Algorithm Integrating Label Signals for Social Event Detection

Zhenguo Yang, Qing Li, Zheng Lu, Yun Ma, Zhiguo Gong, Haiwei Pan

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

5 Citations (Scopus)

Abstract

Photo-sharing social media sites provide new ways for users to share their experiences and interests on the Web, which aggregate large amounts of multimedia resources associated with a wide variety of real-world events in different types and scales. In this work, we aim to tackle social event detection from these large amounts of image collections by devising a semi-supervised multimodal clustering algorithm, denoted by SSMC, which exploits label signals to guide the fusion of the multimodal features. Particularly, SSMC takes advantage of the distribution over the similarities on a small amount of labeled data to represent the images, fusing multiple heterogeneous features seamlessly. As a result, SSMC has low computational complexity in processing multimodal features for both initial and updating stages. Experiments are conducted on the Mediaeval social event detection challenge, and the results show that our approach achieves better performance compared with the baseline algorithms.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages32-39
Number of pages8
ISBN (Electronic)9781479986880
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event1st IEEE International Conference on Multimedia Big Data, BigMM 2015 - Beijing, China
Duration: 20 Apr 201522 Apr 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015

Conference

Conference1st IEEE International Conference on Multimedia Big Data, BigMM 2015
CountryChina
CityBeijing
Period20/04/1522/04/15

Keywords

  • Multimedia
  • Multimodal clustering
  • Social event detection
  • Social media

ASJC Scopus subject areas

  • Information Systems
  • Media Technology

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