Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections

Zhenguo Yang, Qing Li, Zheng Lu, Yun Ma, Zhiguo Gong, Haiwei Pan and Yangbin Chen
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Zhenguo Yang: Department of Computer Science, City University of Hong Kong, Hong Kong
Qing Li: Department of Computer Science, City University of Hong Kong, Hong Kong
Zheng Lu: Department of Computer Science, City University of Hong Kong, Hong Kong
Yun Ma: Department of Computer Science, City University of Hong Kong, Hong Kong
Zhiguo Gong: Department of Computer and Information Science, University of Macao, Taipa, Macao
Haiwei Pan: College of Computer Science and Technology, Harbin Engineering University, Harbin, China
Yangbin Chen: Department of Computer Science, City University of Hong Kong, Hong Kong

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2015, vol. 6, issue 4, 1-22

Abstract: In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines.

Date: 2015
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