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28-10-2016

Dual graph regularized NMF model for social event detection from Flickr data

Authors: Zhenguo Yang, Qing Li, Wenyin Liu, Yun Ma, Min Cheng

Published in: World Wide Web | Issue 5/2017

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Abstract

In this work, we aim to discover real-world events from Flickr data by devising a three-stage event detection framework. In the first stage, a multimodal fusion (MF) model is designed to deal with the heterogeneous feature modalities possessed by the user-shared data, which is advantageous in computation complexity. In the second stage, a dual graph regularized non-negative matrix factorization (DGNMF) model is proposed to learn compact feature representations. DGNMF incorporates Laplacian regularization terms for the data graph and base graph into the objective, keeping the geometry structures underlying the data samples and dictionary bases simultaneously. In the third stage, hybrid clustering algorithms are applied seamlessly to discover event clusters. Extensive experiments conducted on the real-world dataset reveal the MF-DGNMF-based approaches outperform the baselines.

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Title: Dual graph regularized NMF model for social event detection from Flickr data
Authors: Zhenguo Yang
Qing Li
Wenyin Liu
Yun Ma
Min Cheng
Publication date: 28-10-2016
Publisher: Springer US
Published in: World Wide Web / Issue 5/2017
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-016-0405-1

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