Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation
	Qian, Shengsheng1,2 ; Zhang, Tianzhu1,2 ; Xu, Changsheng1,2 ; Hossain, M. Shamim 3; Shengsheng Qian; Changsheng Xu; Tianzhu Zhang
刊名	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
	2014-12-01
卷号	11 期号:2
关键词	Algorithms Experimentation Performance Social event classification multimodality supervised LDA AdaBoost social media
英文摘要	With the rapidly increasing popularity of social media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and text) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to exactly find the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multimodal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification by integrating a supervised topic model, denoted as multi-modal supervised Latent Dirichlet Allocation (mm-SLDA), in the boosting framework. Our proposed BMM-SLDA has a number of advantages. (1) Our mm-SLDA can effectively exploit the multimodality and the multiclass property of social events jointly, and make use of the supervised category label information to classify multiclass social event directly. (2) It is suitable for large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset of data to efficiently train the corresponding topic models. (3) It effectively exploits social event structure by the document weight distribution with classification error and can iteratively learn new topic model to correct the previously misclassified event documents. We evaluate our BMM-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
研究领域[WOS]	Computer Science
关键词[WOS]	RECOGNITION ; ANNOTATION
收录类别	SCI
语种	英语
WOS记录号	WOS:000348308800004
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/2822]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者	Changsheng Xu
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.China Singapore Inst Digital Media, Singapore 119613, Singapore 3.King Saud Univ, Coll Comp & Informat Sci, SWE Dept, Riyadh, Saudi Arabia
推荐引用方式 GB/T 7714	Qian, Shengsheng,Zhang, Tianzhu,Xu, Changsheng,et al. Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2014,11(2).
APA	Qian, Shengsheng.,Zhang, Tianzhu.,Xu, Changsheng.,Hossain, M. Shamim.,Shengsheng Qian.,...&Tianzhu Zhang.(2014).Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,11(2).
MLA	Qian, Shengsheng,et al."Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 11.2(2014).