Dual structure constrained multimodal feature coding for social event detection from flickr data

Z. Yang, Qing Li, Z. Lu, Y. Ma, Z. Gong, W. Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

© 2017 ACM.In this work, a three-stage social event detection (SED) framework is proposed to discover events from Flickr-like data. First, multiple bipartite graphs are constructed for the heterogeneous feature modalities to achieve fused features. Furthermore, considering the geometrical structures of dictionary and data, a dual structure constrained multimodal feature coding model is designed to learn discriminative feature codes by incorporating corresponding regularization terms into the objective. Finally, clustering models utilizing density or label knowledge and data recovery residual models are devised to discover real-world events. The proposed SED approach achieves the highest performance on the MediaEval 2014 SED dataset.
Original languageEnglish
Article number19
JournalACM Transactions on Internet Technology
Volume17
Issue number2
DOIs
Publication statusPublished - 1 Mar 2017
Externally publishedYes

Keywords

  • Event detection
  • Feature coding
  • Multimedia content analysis
  • Multimodal fusion
  • Social multimedia analytics

ASJC Scopus subject areas

  • Computer Networks and Communications

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